Capítulo 9 Classificação

Davi Moreira, Mônica Rocabado

Como apresentado por Grimmer e Stewart (2013), os métodos de classificação automatizada, podem ser divididos em dois grupos:

Categorias conhecidas e
Categorias desconhecidas.

Quadro Grimmer e Stweart (2013)

9.1 Categorias conhecidas

Ancorados na teoria, na experiência ou especialidade em determinado assunto pode-se classificar um acervo de documentos em categorias já conhecidas. As aplicações são inúmeras, mas entre elas descatam-se duas:

Métodos de dicionário; e
Métodos de aprendizagem supervisionada (supervised learning methods).

9.1.1 Método de dicionário

O método de dicionário se refere, de forma ampla, ao uso tanto de padrões de texto em palavras chaves, quanto do uso de buscas booleanas complexas e expressões regulares para contar com que frequência certos conceitos e/ou códigos e/ou padrões ocorrem no documento analisado (Moreira, Pires, and Medeiros 2022). Esta é uma abordagem dedutiva, pois o dicionário define a priori quais códigos serão mensurados e, com isto, não é afetado pelos dados a serem analisados.

O uso combinado do método com técnicas de Processamento de Linguagem Natural (PLN) torna possível análises mais aprofundadas do corpus, como o expressões de sentimento atribuídas a atores específicos, ou ações e afetos de um ator em relação a outro (Atteveldt et al. 2017).

9.1.1.1 Análise de sentimento

Como destacam Izumi e Moreira (2018), a análise de sentimentos pode ser realizada com o uso de um dicionário de termos. O pacote quanteda possui funções que auxiliam nessa tarefa. Através das funções tokens_lookup() ou dfm_looup() pode-se contar os valores do dicionário. O quanteda contém o Lexicoder Sentiment Dictionary criado por Young e Soroka (2012). Ver também: quanteda.sentiment para textos em inglês.

Para a língua portuguesa, o Grupo de Processamento da Linguagem Natural da Pontifícia Universidade Católica do Rio Grande do Sul (PUC-RS) possui o OpLexicon Vieira and Souza (2011). Esse dicionário apresenta 31.719 termos dos quais 14.254 carregam sentimentos negativos, 8.469 sentimentos positivos e 8.996 sentimentos neutros.

Usando a base de dados do jornal The Guardian presente no pacote quanteda, iremos aplicar o método para analisar os sentimentos nos artigos que mencionam a União Europeia. Para uso da base de dados é necessário o pacote quanteda.corpora, que pode ser baixada através de devtools::install_github("quanteda/quanteda.corpora").

library(quanteda)
library(quanteda.corpora)
library(stringi)
library(stringr)
library(lubridate)
library(tidyverse)
library(here)

corp_news <- download("data_corpus_guardian")

Com o corpus, incluímos as variáveis referentes ao ano, mês e semana, tendo como base a variável date. Em seguida, filtramos o corpus para obter artigos de 2016 até o período mais recente da base.

# Criando novas variáveis ao nível do documento
docvars(corp_news, 'year') <- year(docvars(corp_news, 'date'))
docvars(corp_news, 'month') <- month(docvars(corp_news, 'date'))
docvars(corp_news, 'week') <- week(docvars(corp_news, 'date'))

# Filtro do variável ano
corp_news <- corpus_subset(corp_news, 'year' >= 2016)

Para análise, devemos processar a base. Assim, vamos transformá-la em tokens, remover as pontuações e selecionar os documentos que mencionam a Únião Europeia.

# Transformando em tokens
toks_news <- tokens(corp_news, remove_punct = TRUE)

# Filtrar os documentos que mencionam a UE
eu <- c('EU', 'europ*', 'european union')
toks_eu <- tokens_keep(toks_news, pattern = phrase(eu), window = 10)

Antes de aplicar o dicionário de sentimento (Lexicoder Sentiment Dictionary), podemos verificar que ele possui quatro códigos, cada qual possuindo termos e expressões referentes a cada um dos códigos.

lengths(data_dictionary_LSD2015)

##     negative     positive neg_positive neg_negative 
##         2858         1709         1721         2860

Como nosso intuito é analisar somente os termos positivos e negativos relacionados aos documentos que mencionam a UE após e em 2016, vamos selecionar somente os dois primeiros códigos.

data_dictionary_LSD2015_pos_neg <- data_dictionary_LSD2015[1:2]

Aplicamos então o dicionário a nossa base previamente tratada com a função tokens_lookup() e a transformamos em um dfm:

#Aplicando o dicionário a nossa base
toks_eu_lsd <- tokens_lookup(toks_eu, dictionary = data_dictionary_LSD2015_pos_neg)

#Transformando em DFM
dfmat_news_lsd <- dfm(toks_eu_lsd)

Para melhor visualização dos dados agrupamos a nossa dfm por semana:

# Agrupando a DFM
dfmat_eu_lsd <- dfm_group(dfmat_news_lsd, groups = week)

#Visualizando os dados
matplot(dfmat_eu_lsd, type = 'l', xaxt = 'n', lty = 1, ylab = 'Frequency')
grid()
axis(1, seq_len(ndoc(dfmat_eu_lsd)), ymd("2016-01-01") + 
       weeks(seq_len(ndoc(dfmat_eu_lsd)) - 1))
legend('topleft', col = 1:2, legend = c('Negative', 'Positive'), lty = 1, bg = 'white')

Podemos também visualizar as menções positiva vs as negativas. Para isso, temos que realizar uma contagem dos tokens (total de features).

n_eu <- ntoken(dfmat_eu_lsd)

plot((dfmat_eu_lsd[,2] - dfmat_eu_lsd[,1]) / n_eu,
     type = 'l', ylab = 'Sentiment', xlab = '', xaxt = 'n')
axis(1, seq_len(ndoc(dfmat_eu_lsd)), ymd("2016-01-01") + 
       weeks(seq_len(ndoc(dfmat_eu_lsd)) - 1))
grid()
abline(h = 0, lty = 2)

9.1.1.1.1 Atividade prática

Repita a análise acima para os tokens c('immig*', 'migra*').

9.1.1.2 Dicionário de tópico - Policy Agenda

CAP

Para além de códigos positivos e negativos, existem outros projetos de dicionários com códigos mais complexos, este é caso do LexiCoder Policy Agenda, dicionário que captura os principais tópicos presentes no projeto Comparative Agenda Policy³.

Para realizar os exemplos abaixo é necessário baixar o dicionário dos códigos referentes ao Policy Agenda em formato .lcd, que pode ser realizado por esse link. Como até o momento os dicionários são somente em inglês ou holandês, iremos analisar a base de debates na Assembéia Geral da ONU, UN General Debate, de 2017 presente no pacote quanteda.corpora.

UNGDspeeches <- quanteda.corpora::data_corpus_ungd2017
dict <- dictionary(file = here("data/policy_agendas_english.lcd"))

Antes de aplicar o dicionário, vamos processar a base, retirando informações no texto que não auxiliam nossa análise como números, pontuações e stop_words. Também agrupamos nossa dfm de acordo com o país que realizou o discurso.

UNGD_token <-  tokens(UNGDspeeches, split_hyphens = TRUE, remove_numbers = TRUE, 
                 remove_punct = TRUE, remove_symbols = TRUE, remove_url = TRUE,
                 include_docvars = TRUE)

UNGD_dfm <- dfm(UNGD_token, tolower = TRUE, remove = stopwords("en"))

UNGD_dfm_p <- dfm_group(UNGD_dfm, groups = country)

Com a base pronta, aplicamos o dicionário através do dfm_lookup e obtemos uma DFM que contém os principais temas dos discursos dos países em 2017.

UNGD_dfm_code <- dfm_lookup(UNGD_dfm_p, dictionary = dict)

doc_id	macroeconomics	civil_rights	healthcare	agriculture	forestry	labour	immigration	education	environment	energy	fisheries	transportation	crime	social_welfare	housing	finance	defence	sstc	foreign_trade	intl_affairs	government_ops	land-water-management	culture	prov_local	constitutional_natl_unity	aboriginal	religion
Afghanistan	0	8	0	0	0	4	2	1	1	1	0	2	1	2	0	1	3	0	0	8	1	1	0	0	0	0	0
Albania	0	8	0	0	0	1	0	0	0	2	0	0	1	0	0	0	2	0	0	7	0	1	0	0	0	0	0
Algeria	0	6	0	0	0	0	0	0	0	0	0	1	4	2	0	0	1	1	0	0	0	0	0	0	1	0	0
Andorra	1	9	0	0	0	0	0	0	11	5	0	0	3	0	0	0	0	0	0	10	0	1	0	0	1	0	0
Angola	0	9	0	0	0	1	0	0	2	0	0	0	0	1	0	0	2	0	2	6	0	0	0	0	0	0	0
Antigua & Barbuda	8	1	1	0	0	7	0	2	0	1	0	0	6	3	1	1	0	0	0	5	0	0	0	0	0	0	0
Argentina	3	7	0	1	0	2	0	0	6	0	0	0	6	4	0	3	0	0	2	7	0	0	0	0	1	0	1
Armenia	0	8	0	0	0	0	0	2	0	0	0	0	2	1	0	0	4	0	0	1	0	0	0	0	0	0	0
Australia	2	7	0	0	1	2	0	0	2	0	0	0	0	1	0	0	2	0	0	8	0	0	0	0	0	0	0
Austria	0	3	0	0	0	0	1	0	0	0	0	1	2	1	0	0	1	0	0	4	0	0	0	0	0	0	0
Azerbaijan	5	8	4	0	0	1	1	0	0	2	0	3	2	2	0	1	3	0	1	3	2	0	0	0	0	0	1
Bahamas	5	12	4	0	0	1	0	0	5	3	0	0	3	2	0	4	0	1	0	12	0	1	0	0	0	0	0
Bahrain	0	4	1	0	0	1	0	0	0	2	0	0	2	2	0	0	3	0	0	5	0	0	0	0	1	0	7
Bangladesh	2	3	2	2	0	3	2	2	2	0	0	0	4	4	0	0	4	1	1	2	1	1	0	0	0	0	0
Barbados	0	9	7	0	0	2	0	0	7	0	1	1	1	0	0	2	0	0	1	7	0	2	0	0	0	0	1
Belarus	0	1	0	0	0	0	0	0	0	0	0	2	0	1	0	1	7	0	0	8	0	1	0	0	0	0	0
Belgium	0	2	0	0	0	0	1	1	1	0	0	0	5	1	0	0	1	0	0	4	0	0	1	0	0	0	0
Belize	13	1	2	0	0	3	1	0	3	1	0	1	8	0	0	5	0	0	1	12	0	0	0	0	1	0	0
Benin	0	1	1	0	0	1	0	0	3	0	0	2	0	3	0	1	0	0	0	1	0	2	0	0	0	0	0
Bhutan	0	1	1	0	1	0	0	0	1	1	0	1	1	13	0	0	1	0	1	11	0	0	0	0	0	0	0
Bolivia	0	5	1	0	0	0	3	0	0	2	0	0	10	1	0	2	6	0	3	5	0	1	1	0	1	0	1
Bosnia & Herzegovina	1	3	0	0	0	1	2	0	1	0	0	0	2	1	0	0	2	0	0	7	0	0	0	0	0	0	0
Botswana	2	9	1	0	0	4	0	0	1	0	0	0	2	5	1	0	3	0	0	3	0	0	0	0	0	0	0
Brazil	3	10	0	0	1	0	3	0	0	2	0	0	5	1	0	3	2	0	0	4	0	0	0	0	1	0	0
Brunei	0	2	2	0	0	1	1	0	3	1	0	0	1	1	0	0	3	0	0	5	0	0	0	0	0	0	0
Bulgaria	0	16	0	0	0	0	3	0	1	0	0	0	0	1	0	0	6	0	0	10	0	1	0	0	0	0	0
Burkina Faso	0	0	0	0	0	0	0	0	0	0	0	0	2	1	0	0	1	0	1	3	0	0	0	0	1	0	0
Burundi	0	7	0	0	0	0	0	0	0	0	0	0	2	0	0	0	2	0	0	2	0	0	0	0	0	0	0
Cambodia	1	10	0	0	0	0	1	1	0	0	0	0	0	3	0	0	1	0	1	5	0	0	0	0	0	0	0
Cameroon	0	0	0	0	1	0	0	0	0	0	0	0	0	6	0	0	3	0	0	2	0	0	0	0	0	0	0
Canada	4	25	2	0	0	0	0	0	4	0	0	0	0	3	3	0	0	0	1	2	0	1	0	0	0	6	0
Cape Verde	0	4	0	0	0	1	1	1	1	1	0	3	5	1	0	0	1	0	0	6	1	1	0	0	1	0	0
Central African Republic	1	2	0	1	0	0	0	0	1	1	0	2	8	3	0	0	2	0	0	2	0	1	0	0	0	0	0
Chad	0	2	0	0	0	1	0	0	0	0	0	1	0	3	0	0	1	0	1	2	0	0	0	0	0	0	1
Chile	1	5	0	0	0	7	0	1	4	3	0	0	3	0	0	0	2	0	0	4	0	0	0	0	1	0	0
China	0	4	0	0	0	0	0	1	0	0	0	4	1	1	0	0	3	0	1	10	0	1	0	0	0	0	0
Colombia	0	3	5	0	0	0	1	1	0	0	0	0	4	2	1	0	6	0	0	1	0	0	0	0	0	0	0
Comoros	0	7	1	0	0	0	0	0	1	6	0	3	1	0	0	0	1	0	0	5	0	2	0	0	0	0	3
Congo - Brazzaville	0	1	3	0	0	0	0	0	2	0	0	2	1	1	0	0	1	0	0	4	0	2	0	0	0	0	0
Congo - Kinshasa	0	1	0	0	2	0	0	0	0	0	0	0	5	0	0	0	1	0	0	2	0	0	0	0	0	0	0
Costa Rica	3	30	3	1	0	10	1	0	11	3	0	2	6	4	0	0	5	0	1	12	0	1	2	0	0	0	1
Côte d’Ivoire	1	1	0	0	0	0	0	0	4	1	0	0	0	0	0	0	2	0	0	8	0	0	0	0	0	0	0
Croatia	0	7	0	0	0	1	3	0	2	0	0	0	3	2	0	0	4	0	0	14	0	0	0	0	0	0	0
Cuba	1	9	2	0	0	1	1	0	1	0	0	1	3	3	1	1	6	0	3	2	0	2	0	0	0	0	0
Cyprus	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	2	0	0	9	0	1	0	0	1	0	0
Czechia	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	2	0	0	0	1
Denmark	1	14	1	0	0	1	1	0	0	0	0	0	0	2	0	0	3	0	0	6	0	1	0	0	0	0	0
Djibouti	2	1	1	0	0	1	0	0	1	0	0	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	1
Dominica	0	0	2	0	0	1	0	0	0	1	0	3	0	1	0	0	8	0	0	5	0	1	0	0	0	0	0
Dominican Republic	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Ecuador	7	3	3	0	0	0	1	0	3	0	0	0	0	2	2	0	3	0	0	2	0	0	1	0	0	0	1
Egypt	0	2	0	0	0	0	2	0	0	0	0	0	2	2	0	0	2	0	0	2	0	2	0	0	0	0	2
El Salvador	2	3	2	0	0	4	0	0	1	0	0	0	3	3	1	1	0	0	0	3	0	0	0	0	0	0	0
Equatorial Guinea	0	4	1	0	0	0	0	0	2	0	0	0	1	1	0	0	2	0	0	3	0	1	0	0	0	0	0
Eritrea	1	1	2	0	0	0	0	0	2	0	0	1	0	2	0	0	2	1	0	1	0	0	0	0	0	0	0
Estonia	0	8	1	0	0	0	1	0	0	0	0	1	1	2	0	0	2	2	0	11	0	1	0	0	0	0	0
Ethiopia	0	0	0	0	0	1	0	0	2	0	0	0	0	7	0	0	3	0	0	11	0	0	0	0	0	0	0
European Union	1	0	0	0	0	0	2	0	0	0	0	0	2	0	0	0	2	1	0	19	0	0	0	0	0	0	1
Fiji	0	0	1	1	0	0	0	1	0	2	0	0	0	0	0	0	0	0	0	14	0	1	0	0	0	0	0
Finland	0	7	5	0	0	0	0	0	0	2	0	1	2	0	0	0	4	0	0	9	0	0	0	0	0	0	0
France	1	5	4	0	0	0	3	2	1	1	0	7	4	1	0	0	6	1	0	6	0	1	0	0	0	0	0
Gabon	2	0	1	0	0	0	0	0	2	0	0	1	3	0	0	1	0	0	2	2	0	1	0	0	0	0	0
Gambia	3	4	1	0	0	2	0	0	1	0	0	1	8	1	0	0	0	0	0	5	0	1	0	0	0	0	1
Georgia	3	3	2	0	0	1	1	0	1	0	0	6	2	1	0	2	1	1	0	11	0	1	1	0	2	0	0
Germany	1	2	0	0	0	0	1	1	0	0	0	2	3	1	0	0	7	0	0	7	0	2	0	0	0	0	0
Ghana	0	0	1	0	0	0	0	2	0	1	0	1	2	2	0	0	0	1	0	3	0	0	0	0	0	0	0
Greece	0	10	0	0	0	0	1	0	0	1	0	0	2	0	0	0	5	0	0	3	0	0	1	0	0	0	0
Grenada	3	2	9	0	0	0	0	0	1	0	0	2	0	0	0	3	2	0	1	7	0	0	1	0	0	0	0
Guatemala	1	6	1	0	0	1	0	0	2	0	0	0	9	1	0	0	2	0	0	2	0	1	0	0	1	0	0
Guinea	1	0	1	0	0	0	1	0	0	3	0	0	1	4	0	1	1	1	0	1	0	0	0	0	0	0	0
Guinea-Bissau	1	5	6	1	0	0	0	0	2	0	0	1	0	8	0	0	0	0	2	1	0	0	0	0	0	0	0
Guyana	0	4	0	0	4	0	2	0	6	0	0	0	10	2	0	0	1	0	1	4	0	1	0	0	0	0	0
Haiti	4	1	5	0	0	0	0	0	3	3	0	1	2	0	1	0	1	0	0	4	1	0	0	0	1	0	0
Honduras	5	6	2	0	0	5	0	0	1	0	0	1	5	6	0	1	0	0	1	2	1	0	0	0	0	0	0
Hungary	0	13	1	0	0	0	3	0	0	0	0	0	0	0	0	0	3	1	0	4	0	0	0	0	0	0	0
Iceland	0	16	3	0	0	0	0	0	1	7	0	0	0	3	0	0	1	0	0	1	0	0	0	0	0	0	0
India	3	5	1	0	1	2	0	1	0	0	0	0	0	6	0	1	1	0	1	2	0	0	0	0	0	0	0
Indonesia	0	1	1	0	0	1	0	0	3	0	0	0	0	3	0	0	1	0	0	17	0	0	0	0	0	0	0
Iran	0	11	0	0	0	0	0	0	2	1	0	5	2	2	0	1	7	0	2	10	0	0	0	0	0	0	1
Iraq	0	4	2	0	0	1	2	1	2	2	0	0	9	0	1	0	5	0	0	2	0	1	0	0	5	0	0
Ireland	0	2	0	0	0	0	0	0	3	2	0	0	0	4	0	0	3	0	0	14	0	3	0	0	0	0	0
Israel	0	0	6	1	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	5	0	0	0	0	0	0	3
Italy	0	5	1	0	0	1	0	0	1	1	0	3	2	1	0	1	2	1	0	9	0	1	0	0	0	0	1
Jamaica	4	1	2	0	0	5	0	1	5	0	0	1	0	0	0	5	3	0	4	10	0	0	0	0	0	0	0
Japan	0	0	1	0	0	1	0	1	0	5	0	0	0	1	0	0	12	0	0	1	0	0	0	0	0	0	0
Jordan	4	0	1	0	1	1	1	0	0	3	0	0	1	0	1	1	2	0	1	6	0	0	0	0	0	0	3
Kazakhstan	1	2	0	0	0	0	0	0	1	6	0	4	0	1	0	0	9	2	0	13	0	0	0	0	0	0	0
Kenya	1	1	5	0	0	0	3	0	4	0	0	0	1	1	0	0	3	0	0	8	0	0	0	0	0	0	0
Kiribati	5	3	3	0	0	3	0	0	3	0	1	0	0	1	0	1	0	0	0	21	0	1	0	0	0	0	4
Kuwait	0	2	0	0	0	0	2	0	1	0	0	0	0	1	0	0	1	0	0	2	0	1	0	0	0	0	1
Kyrgyzstan	1	7	1	0	0	0	2	0	2	4	0	1	1	1	0	0	2	0	1	5	1	3	0	0	0	0	1
Laos	0	2	0	0	0	1	0	1	3	0	0	2	0	2	0	0	0	0	1	3	0	0	0	0	0	0	0
Latvia	0	3	0	0	0	0	0	0	2	0	0	0	2	2	0	0	1	0	0	15	0	1	0	0	0	0	0
Lebanon	0	7	1	0	0	3	8	0	1	0	0	0	4	2	0	2	17	0	0	1	0	0	0	0	0	0	1
Lesotho	3	4	6	0	0	3	2	0	2	1	0	1	2	5	0	0	6	0	1	6	0	1	0	0	0	0	2
Liberia	0	2	4	0	0	0	3	1	1	1	0	2	1	2	0	1	1	0	0	5	0	2	0	0	0	0	0
Libya	0	8	0	0	0	0	1	0	0	0	0	1	0	0	0	0	3	0	1	0	0	1	0	0	0	0	0
Liechtenstein	0	5	0	0	0	0	1	1	0	0	0	0	15	0	0	0	4	0	0	2	0	1	1	0	0	0	0
Lithuania	0	1	0	0	0	0	1	1	0	1	0	0	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0
Luxembourg	1	10	5	0	0	1	2	0	1	0	0	0	4	0	0	0	4	0	0	3	0	4	0	0	1	0	0
Macedonia	0	6	0	0	0	1	1	0	2	0	0	0	0	0	0	0	5	0	0	4	0	0	0	0	0	0	0
Madagascar	5	1	11	1	0	2	0	1	2	2	0	0	0	5	0	2	0	0	0	11	0	0	0	0	0	0	0
Malawi	0	3	1	3	0	1	0	1	0	0	0	0	1	1	0	0	1	0	0	9	0	1	0	0	0	0	0
Malaysia	0	2	0	0	0	2	0	0	2	0	0	0	1	0	0	0	5	0	0	3	0	0	0	0	0	0	0
Maldives	2	11	5	0	0	3	1	0	2	7	2	0	1	1	3	0	5	0	0	8	0	1	0	0	1	0	0
Mali	2	2	10	0	0	0	1	0	3	0	0	0	5	1	0	0	0	1	0	5	0	2	0	0	0	0	0
Malta	0	14	0	0	0	1	0	1	1	0	0	0	3	4	0	0	1	0	0	3	0	2	0	0	1	0	0
Marshall Islands	0	2	3	0	0	0	0	1	1	1	1	2	1	1	0	1	3	0	0	8	0	1	0	0	0	0	0
Mauritania	0	7	0	0	0	0	1	0	0	2	0	1	3	0	0	1	0	0	0	0	0	0	0	0	0	0	0
Mauritius	0	1	0	0	0	5	0	1	0	0	0	0	7	0	0	1	0	0	0	1	0	0	0	0	0	0	0
Mexico	2	5	1	1	0	1	0	0	0	0	0	0	0	1	0	2	1	0	0	3	0	0	0	0	0	0	0
Micronesia (Federated States of)	0	4	1	0	0	0	0	0	4	1	1	0	0	1	0	0	0	0	0	8	0	0	0	0	0	0	0
Moldova	0	3	0	0	0	0	0	0	1	1	0	0	0	2	0	2	1	0	0	2	0	0	0	0	1	0	0
Monaco	0	5	2	0	0	0	0	0	2	0	0	1	1	3	0	0	1	0	0	4	0	1	0	0	0	0	0
Mongolia	0	4	1	0	1	2	3	0	0	7	0	5	2	2	0	1	4	0	2	12	0	0	0	0	0	0	0
Montenegro	0	15	0	0	0	0	1	0	0	0	0	0	0	3	0	0	5	0	0	4	0	0	0	0	0	0	0
Morocco	1	1	0	0	0	1	1	0	0	3	0	1	2	1	0	1	2	0	0	5	0	0	0	0	0	0	1
Mozambique	1	3	0	0	0	1	0	0	2	1	0	0	0	1	0	0	2	0	2	4	0	1	0	0	1	0	0
Myanmar (Burma)	0	4	0	0	0	0	1	0	0	0	0	2	5	1	0	0	1	0	0	1	0	0	0	0	0	0	2
Namibia	0	4	0	1	0	3	0	0	0	0	0	0	1	7	0	0	4	0	1	3	0	2	1	0	0	0	0
Nauru	0	1	3	2	0	2	0	1	0	1	0	1	0	0	1	0	1	0	0	4	0	0	0	0	0	0	1
Nepal	0	15	0	0	0	0	0	2	1	1	0	1	4	2	0	0	1	0	0	8	0	0	0	2	5	0	0
Netherlands	0	1	0	0	0	0	0	1	1	0	0	0	3	3	0	0	2	0	0	3	0	0	0	0	0	0	0
New Zealand	0	0	0	0	0	2	1	0	0	4	0	0	0	0	0	1	6	0	0	2	0	3	0	1	0	0	0
Nicaragua	0	5	0	0	0	0	0	0	0	0	0	0	11	7	0	0	2	0	1	2	0	1	0	0	0	0	0
Niger	0	3	0	0	0	0	0	1	2	0	0	0	3	1	0	0	2	0	1	0	0	0	0	0	1	0	1
Nigeria	0	3	0	0	0	1	0	0	0	0	0	0	1	0	0	0	2	0	0	1	0	0	0	0	0	0	0
North Korea	0	0	0	0	0	0	0	0	0	0	0	1	13	0	0	0	18	1	1	2	0	1	0	0	0	0	0
Norway	2	2	1	0	0	0	0	2	6	0	0	2	0	4	0	0	4	0	2	13	0	0	0	0	0	0	0
Oman	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	3	0	2	6	0	3	1	0	0	0	0
Pakistan	1	5	1	0	0	0	5	0	0	1	0	2	4	2	0	0	15	0	1	9	1	1	0	0	0	0	0
Palau	0	1	0	0	0	0	0	0	3	0	1	1	0	1	0	0	0	0	0	10	0	1	0	0	1	0	0
Palestinian Territories	0	6	1	0	0	0	1	0	0	1	0	1	7	0	0	0	1	0	0	3	0	1	0	0	0	0	9
Panama	4	0	3	0	0	1	0	0	0	0	0	4	10	3	1	0	2	0	0	0	0	1	0	0	0	0	3
Papua New Guinea	1	3	4	0	0	0	1	1	1	0	1	3	0	0	0	1	2	0	0	9	0	0	0	0	0	0	0
Paraguay	1	7	0	0	1	0	0	1	0	1	0	2	4	9	2	1	0	0	0	9	0	2	0	0	1	0	2
Peru	0	5	0	0	0	0	0	0	1	0	0	0	0	1	0	0	2	0	0	3	0	0	0	0	0	0	0
Philippines	1	20	2	1	0	0	0	1	3	0	0	0	5	4	0	0	9	0	0	5	0	0	0	1	0	0	1
Poland	0	6	1	0	0	1	1	1	3	1	0	0	0	2	0	0	4	0	0	4	0	1	0	0	0	0	1
Portugal	0	11	1	0	0	1	0	0	3	0	0	0	0	0	0	0	1	0	0	10	0	1	0	0	0	0	0
Qatar	0	7	1	0	0	0	1	0	0	0	0	0	2	0	0	0	4	0	0	1	0	0	0	0	0	0	1
Romania	0	4	0	0	0	0	0	0	0	0	0	0	2	1	0	0	2	0	0	4	0	0	0	0	0	0	0
Russia	0	5	2	0	0	0	1	0	0	1	0	1	1	0	0	0	10	1	1	7	0	1	0	0	0	1	1
Rwanda	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0
Samoa	0	4	0	0	0	0	0	0	4	0	0	5	4	1	0	0	1	0	0	10	0	1	0	0	0	0	0
San Marino	0	17	1	0	0	0	0	0	0	2	0	1	4	5	0	0	1	0	0	7	0	0	0	0	0	0	0
São Tomé & Príncipe	0	4	0	0	0	0	0	0	2	0	0	0	0	1	0	0	1	0	1	8	0	4	0	0	0	0	0
Saudi Arabia	0	1	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Senegal	1	1	0	0	0	0	0	1	1	0	0	0	0	1	0	0	3	0	0	3	0	0	0	0	0	0	3
Serbia	3	2	0	0	0	2	1	0	0	0	0	0	1	1	0	2	1	0	0	8	0	0	0	0	0	0	0
Seychelles	2	1	0	0	0	0	1	0	4	0	0	1	0	0	0	0	0	0	0	4	0	0	0	0	0	0	0
Sierra Leone	0	5	6	0	0	2	1	0	2	1	0	0	2	0	2	0	5	0	0	8	0	0	0	0	0	0	0
Singapore	3	1	2	0	0	0	0	0	2	0	0	0	0	1	0	0	2	1	1	13	0	1	0	0	1	0	0
Slovakia	0	0	0	0	0	0	1	0	4	0	0	0	0	2	0	0	1	0	0	1	0	1	0	0	0	0	0
Slovenia	0	11	1	0	0	0	1	0	3	0	0	1	7	0	0	0	3	0	0	11	0	0	0	0	0	0	0
Solomon Islands	1	11	1	0	0	2	0	0	2	2	1	0	2	1	0	1	2	0	0	10	0	0	1	0	0	0	0
Somalia	2	7	1	3	0	0	2	0	1	0	0	2	2	2	0	0	1	0	1	2	0	3	0	0	0	0	0
South Africa	3	3	3	0	0	0	0	0	1	2	0	0	0	2	1	0	4	0	0	9	0	1	0	0	0	0	0
South Korea	0	2	0	0	0	1	1	0	0	1	0	0	0	0	0	0	18	0	0	3	0	2	4	0	1	0	0
South Sudan	0	0	0	2	0	0	1	0	3	0	0	1	1	0	0	0	3	0	0	0	0	1	0	0	0	0	0
Spain	0	9	0	0	0	0	0	0	0	0	0	1	1	1	0	1	6	0	0	3	0	3	0	0	2	1	0
Sri Lanka	1	12	0	0	0	0	0	0	0	0	0	0	0	3	0	0	3	0	0	1	0	0	0	0	1	0	0
St. Kitts & Nevis	1	1	3	1	0	2	0	1	1	2	0	1	1	1	0	0	4	0	1	7	0	0	0	0	0	0	0
St. Lucia	0	1	1	0	0	1	0	0	2	0	0	0	1	0	1	0	0	0	0	6	0	1	0	1	0	0	0
St. Vincent & Grenadines	0	3	3	0	0	2	1	0	0	3	0	0	0	0	0	2	4	0	0	9	0	1	0	0	0	0	1
Sudan	4	1	0	0	0	0	0	0	1	0	0	2	22	1	0	0	2	0	0	1	0	0	0	0	0	0	0
Suriname	4	6	3	0	2	2	0	0	3	0	0	1	1	0	1	0	1	0	3	10	0	2	2	0	1	1	1
Swaziland	0	1	11	1	0	3	0	3	2	0	0	0	0	2	0	2	0	0	0	5	0	1	0	0	0	0	0
Sweden	4	6	0	0	0	2	0	1	0	0	0	0	0	2	0	0	2	0	0	5	0	0	0	0	0	0	0
Switzerland	0	2	0	0	0	0	1	0	0	0	0	1	0	1	0	0	2	2	0	5	0	1	0	0	0	0	0
Syria	0	1	4	0	0	0	1	0	0	1	0	0	1	0	0	0	20	1	0	3	0	0	1	0	0	0	0
Tajikistan	1	1	0	0	0	0	1	0	0	6	0	5	1	1	0	0	1	0	0	1	0	0	0	0	0	0	2
Tanzania	2	0	0	0	0	3	0	0	0	0	0	0	0	3	0	0	4	0	0	3	0	1	0	0	0	0	0
Thailand	0	3	14	0	0	0	0	2	2	0	0	0	1	2	0	0	0	0	0	2	0	0	0	0	1	0	0
Timor-Leste	0	2	4	0	0	0	1	3	0	0	0	0	3	2	0	0	3	0	1	2	0	1	0	0	0	0	0
Togo	2	5	8	0	0	0	0	2	1	1	0	0	1	1	0	0	1	0	0	1	0	0	0	0	3	0	0
Tonga	0	3	2	0	0	0	0	0	5	7	0	0	0	0	0	0	0	0	0	2	0	1	0	0	0	0	1
Trinidad & Tobago	1	8	1	0	0	1	0	0	5	0	0	0	6	1	0	0	1	0	3	8	0	0	0	0	1	0	0
Tunisia	1	8	0	0	0	1	1	0	1	0	0	1	4	1	0	0	2	0	0	0	0	0	0	0	1	0	0
Turkey	1	6	2	0	0	1	1	0	1	0	0	0	2	4	0	0	2	0	0	3	0	1	0	0	0	0	5
Turkmenistan	0	3	0	0	0	0	1	0	2	7	0	4	0	0	0	0	0	0	0	14	0	1	5	0	0	0	0
Tuvalu	0	18	3	0	0	0	1	1	5	2	0	1	1	0	0	0	0	0	0	17	0	1	0	0	0	0	1
Uganda	0	0	1	3	0	0	0	0	0	0	0	1	1	0	0	0	2	1	0	4	0	2	0	0	0	0	1
Ukraine	0	9	3	0	0	0	5	1	0	2	0	0	15	1	0	0	7	0	0	4	0	0	1	0	0	0	0
United Arab Emirates	0	1	2	0	0	0	0	0	1	1	0	0	2	0	0	0	0	0	0	2	0	1	0	0	0	0	2
United Kingdom	2	3	1	0	0	1	1	0	0	1	0	0	3	0	0	0	3	3	0	15	0	0	0	0	0	0	1
United States	2	6	4	0	0	2	2	3	0	1	0	2	5	3	0	1	6	2	0	5	3	1	0	0	3	0	5
Uruguay	0	4	3	0	0	0	0	0	0	1	0	0	5	2	0	0	12	0	0	9	0	0	0	0	0	0	0
Uzbekistan	1	8	0	0	0	3	1	0	0	1	0	2	0	0	0	0	0	0	1	3	1	0	1	0	0	0	6
Vanuatu	1	8	5	0	0	0	0	1	0	1	0	1	1	4	0	0	3	0	1	3	0	0	0	0	0	0	1
Vatican City	0	13	0	0	0	0	2	0	6	0	0	0	3	6	1	0	20	0	0	4	0	1	0	0	0	0	23
Venezuela	2	7	2	0	0	1	1	0	0	2	0	0	7	3	1	0	7	0	1	2	1	4	0	0	1	0	0
Vietnam	0	2	1	0	0	2	0	0	0	0	0	1	0	2	0	0	3	0	1	6	0	0	0	0	0	0	0
Yemen	0	1	0	0	0	0	0	1	0	0	0	1	1	1	0	0	4	0	0	0	0	0	0	0	0	0	3
Zambia	1	5	2	0	0	0	0	0	4	0	0	1	7	3	0	0	0	0	0	1	0	0	0	0	0	0	0
Zimbabwe	0	1	0	0	0	0	0	1	1	0	0	0	1	3	0	0	2	1	0	8	0	0	0	0	0	0	1

Outra forma de visualizar a dfm é convertendo-a em data.frame. Assim, facilitamos o uso do pacote ggplot2 para produção de gráficos e visualizações.

UNGD_df <- convert(UNGD_dfm_code, "data.frame") %>%
  cbind(docvars(UNGD_dfm_code)) %>%
  gather(macroeconomics:religion, key = "Topic", value = "Share") %>%
  select(country, Topic, Share, continent) %>% 
  group_by(country) %>%
  mutate(Share = Share / sum(Share)) %>%
  mutate(Topic = as_factor(Topic))

Com a base codificada em formato data.frame, podemos visualizar a proporção dos principais tópicos debatidos na ONU ao longo do ano pelo Brasil.

UNGD_df %>%
filter(country == 'Brazil',
       Share != 0) %>% 
  mutate(Topic = fct_reorder(Topic, Share)) %>% 
  ggplot(aes(Topic, Share)) +
  geom_bar(stat = "identity") +
  labs(title = "Distribuição dos tópicos da PA no discurso do Brasil na ONU em 2017",
       subtitle = 'UN General Debate corpus',
       x = '', y ='') +
  coord_flip()

9.1.1.3 Construindo seu próprio dicionário

A função dictionary() do pacote quanteda permite a construção de um dicionário de códigos pelo usuário. No exemplo abaixo, iremos utilizar a base dos votos proferidos pelos deputados federais na sessão de aprovação do impeachment da Presidenta Dilma Rousseff em Abril de 2016.

Para baixar os dados do Impeachment utilize o pacote txt4cs

#devtools::install_github("davi-moreira/txt4cs-pkg", force = T) download do pacote caso ainda não o tenha

impeachment_dilma <- txt4cs::impeachment_dilma

Utilizando a função dictionary(), vamos criar um dicionário com expressões específicas.

dict_impeach <- dictionary(list(golpe = "golp", 
                                deus = c("deus", "jesus", "cristo", 
                                         "cristao"),
                          familia = c("familia", "filh(o|a)", 
                                      "net(o|a)", "avo", "pai", "mae"),
                          patria = c("patria", "nacao")))

A seguir, processamos a base para obter nossa dfm.

impeachment_dfm_party <- impeachment_dilma %>%
  mutate(text = stri_trans_general(text, "Latin-ASCII")) %>%
#  filter(partido == "PSL") %>% select(nomeOrador, text)
 corpus(docid_field = "doc_id", text_field = "text") %>%
#  print(impeachment_dfm_party, max_ndoc = 3, max_nchar = 50)
  tokens(remove_punct = TRUE, remove_numbers = T) %>%
  tokens_tolower() %>%
  tokens_remove(stopwords(source = "stopwords-iso", language = "pt"), min_nchar = 2) %>%
  # tokens_wordstem(language = "pt") %>%
  dfm() %>%
  dfm_remove(pattern = c('sr', 'senhor',
                         'presidente', 'presidencia', 
                         'esclarecimentos', 'total', 'deputado*',
                         'beto', 'mansur', 'palma*', 
                         'eduardo', 'cunha', 'vota*',
                         'process*', 'discurs*', 'lido*')) %>%
  dfm_group(groups = partido)

Em seguida, aplicamos o nosso dicionário criado.

#Aplicando o dicionário

dict_dtm <- dfm_lookup(impeachment_dfm_party, dictionary = dict_impeach, 
                       valuetype = "regex", nomatch = "_unmatched")

Podemos transformar a nossa DFM em um data.frame para usar a infraestrutura de processamento do projeto tidyverse. Dessa forma, com pacote ggplot2 visualizamos quais partidos mais mencionaram os códigos do dicionário.

impeach_df <- convert(dict_dtm, "data.frame") %>%
  bind_cols(docvars(dict_dtm)) %>% 
  pivot_longer(golpe:patria, names_to = "topic", values_to = "n") %>%
  select(partido, topic, n) %>% 
  group_by(partido, topic) %>% 
  summarise(count = sum(n)) %>% 
  mutate(perc = count/sum(count)) %>% 
  mutate(topic = factor(topic, levels = c("golpe", 
                                              "patria",
                                              "familia",
                                              "deus"))) %>%
  group_by(partido) %>% 
  arrange(topic, perc)

cores <- c("#000000",
           "#787878",
            "#C0C0C0",
           "#DCDCDC")


impeach_df %>% 
  mutate(partido = factor(partido, levels = unique(impeach_df$partido))) %>%
  ggplot(aes(x = partido, y = perc, fill = topic)) +
    geom_bar(stat="identity", width = 0.7) +
    scale_fill_manual (values = cores) +
    coord_flip() + theme_bw() + 
    labs(title = 'Termos mencionados nos disursos por partido', fill = 'Tópico') +
    theme(axis.title.x = element_blank(), 
        axis.title.y = element_blank(),
        #        legend.key = element_rect(color = "gray", fill = "black"),
        legend.title = element_blank()
        )

9.1.2 Ressalvas

Segundo Grimmer e Stewart (2013), o objetivo principal do método é automatizar a codificação manual dos documentos a partir de categorias ou métricas previamente definidas. Logo, se o método estiver perfomando bem, irá replicar a codificação manual. Por isso, o método de validação recomendado é a comparação do resultado da codificação da máquina com aqueles obtidos através de codificação manual do seguinte modo:

Realiza-se o ajuste inicial do modelo aplicado ao training set;
Um segundo conjunto de documentos manualmente codificados seriam usados para avaliar o desempenho do modelo utilizado no passo 1;
Definido o modelo final, este é aplicado ao test set para completar a classificação do acervo.

A validação cruzada pode ser utilizada para replicar esse procedimento ideal.

9.1.3 Métodos de aprendizagem supervisionada

Métodos de aprendizado supervisionado replicam a familiar tarefa de codificação manual, porém com redução de custos e ganho de escala. Sua implementação pressupõe a classificação manual de uma amostra do acervo em um conjunto predeterminado de categorias. Essa amostra classificada, conhecida como conjunto de treinamento ou training set, é usada para treinar modelos estatísticos, cuja principal aplicação é a classificação do restante do acervo, o conjunto de teste ou test set. Ao final da classificação, procedimentos de validação devem ser adotados para se averiguar a performance do modelo utilizado.

Métodos de aprendizado supervisionado requerem que os pesquisadores desenvolvam regras de codificação manual para as categorias de interesse. Tal necessidade força os analistas a desenvolverem definições coerentes de conceitos para aplicações particulares, o que leva à clareza sobre a classificação pretendida. Outra vantagem dos métodos de aprendizado supervisionado para classificação é a facilidade de validação e verificação da performance do modelo utilizado.

Como apontam Grimmer e Stewart (2013), todos os métodos de aprendizagem supervisionada pressupõem três etapas básicas após os procedimentos de pré-processamento:

Construir um conjunto de treinamento (training set)

Esquema de codificação: Para a construção de um conjunto de treinamento, deve ser criado um esquema/plano de codificação manual que supere dificuldades relacionadas a ambiguidades na linguagem, a atenção limitada dos codificadores e o entendimento sobre conceitos presentes no acervo. Com um livro de códigos elaborado, devem-se realizar exercícios de testes para que sejam identificadas ambiguidades no esquema de codificação ou nas categorias negligenciadas. Esse procedimento leva, subsequentemente, a uma revisão do livro de códigos, que então precisa ser aplicado a um novo conjunto de documentos para assegurar que as ambiguidades tenham sido suficientemente resolvidas. Logo, somente após os codificadores aplicarem o esquema de codificação aos documentos do acervo sem perceber ambiguidades, o esquema estará pronto para ser aplicado ao restante do conjunto de dados^[Para mais detalhes, ver Krippendorff (2004), Neuendorf (2002) e a documentação disponível no pacote ReadMe de Hopkins e King (2010).
Seleção do conjunto de treinamento: Idealmente, os documentos presentes no conjunto de treinamento devem ser representativos do acervo. Logo, para um bom desempenho do modelo, é aconselhável que o conjunto de treinamento seja construído a partir de uma amostra aleatória da coletânea à qual pertencem. Isto posto, resta saber qual a quantidade ideal de documentos para o conjunto de treinamento. Hopkins e King (2010) indicam quinhentos como regra geral, sendo cem documentos já suficientes para alguns casos. No entanto, o número ideal dependerá da aplicação específica de interesse, pois, conforme o número de categorias aumenta, mais documentos são necessários em cada categoria do conjunto de treinamento para uma boa performance do modelo.

Aplicar o método de aprendizado supervisionado

Os métodos de aprendizagem supervisionada são diversos, mas compartilham de uma estrutura comum. Cada modelo de aprendizagem supervisionada assume que existe uma função \(f\), não observada, que associa o vocabulário de palavras dos documentos às categorias preestabelecidas. Assim, com base no conjunto de treinamento, o algoritmo “interpreta” essa associação e a replica aos demais documentos.

Validar, validar, validar

Como já vimos, a validação é extremamente importante quando utilizamos métodos de aprendizagem computacional. Para modelos de classificação supervisionada, esse requisito não é diferente. Veremos adiante com mais detalhes como empreender essa tarefa através de modelos de classificação supervisionada.

9.1.3.1 Naive Bayes

Similar ao método de dicionário, a classificação pelo método Naive Bayes também requer uma categorização prévia do texto em \(k\) categorias. Logo, para seu funcionamento, exige que a base seja dividida entre training set e test set. O conjunto de treinamento (training set) é usado para aprender sobre a distribuição de palavras dos documentos de cada categoria \(k\). Essa distribuição é usada para classificar cada um dos documentos no conjunto restante do acervo (test set). Esse é um dos mais simples e poderosos métodos de classificação individual por não demandar tempo ou memória excessiva da máquina.

Teorema de Bayes

Com base no teorema de Bayes⁴, o modelo infere a probabilidade de que o documento \(i\) pertença à categoria \(k\), dado o perfil de palavras \(P_i\). Sendo \(C_k\) uma categoria qualquer, pelo teorema de Bayes temos \(P(C_k|P_i) \propto P(C_k)P(P_i|C_k)\). Logo, é necessário estimar \(P(C_k)\) e \(P(P_i|C_k)\). Após o modelo ser treinado, essas probabilidades são passadas ao test set que irá calcular a probabilidade de cada termo pertencer a cada categoria. O modelo opera a classiﬁcação ao inferir a probabilidade de que o documento \(i\) pertença à categoria \(k\), dado o perﬁl de palavras \(P_i\).

Sendo o conjunto de treinamento uma boa representação do acervo, temos que o estimador de máxima verossimilhança de \(P(C_k)\) é dado pela proporção de documentos do conjunto de treinamento em cada categoria \(k\). Por sua vez, a estimação de \(P(P_i|C_k)\) é mais complexa e necessita do pressuposto ingênuo (naive assumption) de que, dada a categoria de um documento, as palavras são geradas de forma independente. Mesmo com esse pressuposto equivocado, o modelo ainda é capaz de capturar informações úteis para classiﬁcação dos documentos⁵ de uma rica literatura que inclui outros modelos como: Random Forests [Breiman (2001), Support Vector Machines (Venables and Ripley 2002) e Redes Neurais (Bishop, Bishop, and Bishop 1996).

Para realizar a estimação com o Naive Bayes é necessário⁶:

Definir uma base de treinamento ( training set ) e outra de teste ( test set ) baseadas no corpus.
Gerar uma DFM baseada nesses dados.
Treinar o algorítmo que será alimentado pela base de treinamento.
Checar a perfomance dos resultados (accuracy).
Comparar os dados com uma predição aleatória.

Como exemplo prático, vamos aplicar o Naive Bayes a um acervo de 2.000 análises críticas de filmes que indicam se este apresenta conotação positiva ou negativa. Neste exemplo, usamos 1.500 comentários como conjunto de treinamento (training set). Em seguida, estimamos as categorias para as revisões restantes (test set).

library(quanteda)
library(quanteda.textmodels)
library(caret)
library(e1071)

Primeiro, obtemos nossa base com críticas dos filmes presente no pacote quanteda.textmodels. A variável sentiment indica se um filme foi classificado como positivo ou negativo.

corp_movies <- quanteda.textmodels::data_corpus_moviereviews

Text	Types	Tokens	Sentences	sentiment	id1	id2
cv000_29416.txt	354	841	9	neg	cv000	29416
cv001_19502.txt	156	278	1	neg	cv001	19502
cv002_17424.txt	276	553	3	neg	cv002	17424
cv003_12683.txt	313	555	2	neg	cv003	12683
cv004_12641.txt	380	841	2	neg	cv004	12641

Para podermos trabalhar com a base é necessário processá-la. Sendo assim, criamos uma variável que corresponde ao id numérico dos filmes classificados. Em seguida transformamos a nossa base em tokens e em uma dfm.

docvars(corp_movies, "id_numeric") <- 1:ndoc(corp_movies)

toks_movies <- tokens(corp_movies, remove_punct = TRUE, remove_number = TRUE) %>% 
               tokens_remove(pattern = stopwords("en")) %>% 
               tokens_wordstem()

dfmt_movie <- dfm(toks_movies)

Para seleção de nossa base de treinamento e de teste, iremos primeiro gerar uma amostra aleatória de 1500 números que podem variar de 1 a 2000. Com este número referente a base de treinamento, filtramos nossa dfm para que o id dos filmes corresponda ao id númerico aleatório, obtendo assim nossa dfm de treinamento. Para dfm de teste o processo é similar, mas filtramos os 500 registros que não correspondem ao id treinamento.

# id aleatório
set.seed(300)
id_train <- sample(1:2000, 1500, replace = FALSE)

# training set
dfmat_training <- dfm_subset(dfmt_movie, id_numeric %in% id_train)

# test set 
dfmat_test <- dfm_subset(dfmt_movie, !id_numeric %in% id_train)

Com a base pronta, treinamos o modelo Naive Bayes através da função textmodel_nb, colocando o resultado no objeto tmod_nb.

tmod_nb <- textmodel_nb(dfmat_training, docvars(dfmat_training, "sentiment"))

Como vimos na explicação da teoria, o modelo Naive Bayes consegue apenas levar em consideração as features que ocorrem tanto na base treinamento, quanto na base de teste. Para isso, é necessário tornar as features identicas em ambas dfms usando a função dfm_match(). No caso, obtemos uma base em que as features da base de teste só permanecem na análise caso também ocorram na base de treinamento.

dfmat_matched <- dfm_match(dfmat_test, features = featnames(dfmat_training))

Agora, vamos criar dois objetos para nos auxiliar na validação do modelo: i) a classe prevista, somente com as 500 classificações da base de teste; e ii) a classe atual, utilizada para o treinamento.

actual_class <- dfmat_matched$sentiment
predicted_class <- predict(tmod_nb, newdata = dfmat_matched)

	x
cv000_29416.txt	neg
cv013_10494.txt	neg
cv032_23718.txt	pos
cv033_25680.txt	neg
cv036_18385.txt	neg
cv038_9781.txt	neg

9.1.3.2 Validar a saída do modelo

Se o método de aprendizagem supervisionada aplicado tiver bom desempenho, ele será capaz de se assemelhar à classificação manual nas tarefas de codificação de documentos em categorias. Esse objetivo claro implica um padrão preciso para sua avaliação, ou seja: a comparação da saída da codificação automatizada com a saída da codificação manual.

Logo, o procedimento de validação ideal se divide em três⁷:

o ajuste inicial do modelo realizado no conjunto de treinamento;
depois que um modelo final é escolhido, um segundo conjunto de documentos codificados manualmente - o conjunto de validação - deve ser usado para avaliar o desempenho do modelo;
em seguida, que o modelo final seja aplicado ao restante do acervo (test set) para completar a classificação.

tab_class <- table(actual_class, predicted_class)

	neg	pos
neg	213	45
pos	37	205

Na tabela cruzada, vemos que o número de falsos positivos e falsos negativos é semelhante. O classificador cometeu erros em ambas as direções, mas não parece superestimar ou subestimar uma classe.

Podemos usar a função confusionMatrix() do pacote caret para avaliar o desempenho da classificação.

confusion <- confusionMatrix(tab_class, mode = "everything")
confusion

## Confusion Matrix and Statistics
## 
##             predicted_class
## actual_class neg pos
##          neg 213  45
##          pos  37 205
##                                           
##                Accuracy : 0.836           
##                  95% CI : (0.8006, 0.8674)
##     No Information Rate : 0.5             
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.672           
##                                           
##  Mcnemar's Test P-Value : 0.4395          
##                                           
##             Sensitivity : 0.8520          
##             Specificity : 0.8200          
##          Pos Pred Value : 0.8256          
##          Neg Pred Value : 0.8471          
##               Precision : 0.8256          
##                  Recall : 0.8520          
##                      F1 : 0.8386          
##              Prevalence : 0.5000          
##          Detection Rate : 0.4260          
##    Detection Prevalence : 0.5160          
##       Balanced Accuracy : 0.8360          
##                                           
##        'Positive' Class : neg             
##

Precision, Recall e F1 são medidas frequentemente usadas para avaliar o desempenho da classificação.

Precision resulta de \(\frac{TP}{TP + FP}\), onde \(TP\) é o número de verdadeiros positivos e \(FP\) os falsos positivos.
Recall divide os falsos positivos pela soma de positivos verdadeiros e falsos negativos \(\frac{TP}{(TP + FN)}\).
A pontuação F1 é uma média harmônica de Precision e Recall \(2\times \frac{(Precision \times Recall)}{Precision + Recall}\).

Podemos visualizar o resultado transformando a matriz em data.frame.

confusion.data <- as.data.frame(confusion[["table"]])

ggplot(confusion.data,
       aes(x = predicted_class, y = actual_class, fill = Freq)) +
  xlab("Predicted class") +
  ylab("Actual class") +
  geom_tile() + theme_bw() + coord_equal() +
  scale_fill_distiller(palette = "Blues", direction = 1)

9.2 Categorias desconhecidas

Vimos que os métodos de aprendizagem supervisionada auxiliam na tarefa de classificação do acervo a partir de um conhecimento prévio sobre suas categorias. Contudo, não é difícil encontrar situações nas quais o conjunto de categorias seja desconhecido. Pode, por exemplo, ser do interesse de um pesquisador identificar quais tópicos são enfatizados pelos deputados e deputadas federais nos discursos proferidos ao longo de diversas legislaturas (Moreira 2020). Uma vez que a atividade do representante político se debruça sobre inúmeras esferas da sociedade e da vida, predeterminar categorias temáticas de fala dos deputados e deputadas federais pode limitar o conhecimento a ser obtido sobre o acervo. Para enfrentar esse desafio, a seguir apresentamos métodos de aprendizado não supervisionado (unsupervised learning methods).

9.2.1 Métodos de aprendizagem não supervisionada

A classe de métodos de aprendizado não supervisionado revela características subjacentes ao texto sem a necessidade de indicação de categorias de interesse. Ao invés de exigir a determinação prévia das categorias, os métodos de aprendizado não supervisionado usam premissas e propriedades de modelagem dos textos para estimar um conjunto de categorias e, simultaneamente, atribuir a elas os documentos (ou partes de seu conteúdo).

Ao informar para o algoritmo o número \(k\) de categorias nas quais os documentos devem ser alocados, há a oportunidade de se descobrir qual a composição de categorias que apresenta melhor aderência ao conteúdo em análise. Dada a incerteza do pesquisador sobre a performance do modelo em relação ao acervo, procedimentos de validação são essenciais.

9.2.1.1 Topic Models: a modelagem de tópicos

Os modelos de tópicos possuem duas principais características:

definem estatisticamente um tópico como função densidade de probabilidade sobre palavras. Para um tópico \(k\) \((k = 1, . . . , K)\), essa função de probabilidade é representada com um vetor \(M \times 1\), \(\theta_k\), em que \(\theta_{mk}\) descreve a probabilidade de o \(k\)−ésimo tópico usar a \(m\)−ésima palavra. Logo, para estimar um tópico, os modelos usam a ocorrência de palavras entre documentos e pressupõem, em sua grande maioria, o uso da DFM obtida através do pré-processamento dos dados.
os modelos de tópicos compartilham uma estrutura hierárquica básica. Como apresenta a Figura 9.1. Para cada modelo temos o elemento sobre o qual os tópicos estão distribuídos. Em outras palavras, o elemento que terá uma distribuição de tópicos que somada resulta em um ou 100%. Em seguida, na parte inferior da hierarquia, palavras ou documentos que são atribuídos aos tópicos. O Expressed Agenda Model (Grimmer 2010), por exemplo, pressupõe que cada documento seja classificado em apenas um tópico.

Figure 9.1: Modelos de Tópicos - (Izumi e Moreira, 2018)

Como apontam Grimmer e Stewart (2013), todos os métodos de aprendizagem não supervisionada pressupõem duas etapas básicas após os procedimentos de pré-processamento dos dados:

1. Definindo o número \(k\) de categorias

Determinar o número de categorias é uma das tarefas mais complexas no aprendizado não supervisionado. Ao mensurar quão bem modelos se ajustam aos dados, medidas de ajuste estatístico tornam-se inúteis diante da brusca redução de informação que o uso de métodos não supervisionados para análise de conteúdo pressupõem após o pré-processamento dos dados. Os textos pré-processados representam uma simplificação substancial dos documentos, sendo o objetivo do uso de métodos não supervisionados a revelação de informações latentes e substantivamente relevantes. Logo, em vez de ajuste estatístico, a seleção de modelos deve ser tratada como um problema de mensuração substantiva. Em linha com Grimmer e Stewart (2013), a abordagem mixed-method fornecida por Quinn et al. (2010) é adequada. Nela, os modelos candidatos são ajustados variando-se o número \(k\) de categorias para, em seguida, ser realizada uma avaliação manual e qualitativa de seleção do modelo final com base na qualidade das categorias obtidas pelos diferentes modelos.

2. Validação

Se, de um lado, o uso da aprendizagem não supervisionada reduz os custos de análise manual do acervo antes da aplicação do modelo, de outro, a carga de trabalho para a validação de seus resultados é relevante. É a validação extensiva das categorias estimadas e dos documentos classificados que permite a realização de inferências concretas sobre o acervo^[Para conhecer um exemplo aplicado referente ao uso do Expressed Agenda Model, veja Moreira (2020). Para conhecer uma abordagem mais recente sobre validação, veja Ying et. al (2022).

9.2.1.1.1 LDA: Latent Dirichlet Allocation

O Latent Dirichlet Allocation (LDA) (D. Blei and Jordan 2003) é um método popular para modelagem de tópicos. Ele trata cada documento como uma mistura de tópicos e cada tópico como uma mistura de palavras. Isso permite que os documentos “se sobreponham” uns aos outros em termos de conteúdo, em vez de serem separados em grupos distintos como seria numa análise de cluster padrão. Em outra palavras:

cada documento consiste em uma distribuição de tópicos. Ou seja, um documento pode estar em mais de um tópico.
cada tópico consiste em uma distribuição de termos (features). Os termos (features) podem estar presentes em mais de um tópico.

Dessa forma, o LDA nos auxilia a encontrar as palavras associadas com cada tópico, enquanto também determina os tópicos e o quanto eles descrevem cada documento. Alguns termos (features) possuem maior probabilidade de aparecer em um tópico e outros menos, o mesmo vale para os documentos.

Vale ressaltar que o resultado não apresenta automaticamente o nome ou rótulo de cada tópico estimado. É necessária uma análise qualitativa para compreender o resultado dos termos em cada tópico e a diferença entre os tópicos, dado que os termos podem se repetir entre os tópicos, mudando a probabilidade em cada um (Ying, Montgomery, and Stewart 2022).

Figure 9.2: Probabilistic Topic Models - LDA (Blei, 2012)

Para aplicação do LDA, faremos uso do pacote topicmodels, que implementa o modelo desenvolvido por Blei et al D. M. Blei (2013). Usaremos uma DFM contida no pacote que possui 2.246 documentos (linhas) e 10.473 (colunas).

library(topicmodels)
library(tidytext)
library(ggplot2)
library(dplyr)
library(tidyr)

data("AssociatedPress")

Vamos utilizar \(k\) categorias igual a 2 para aplicar nosso modelo de LDA na base AssociatedPress

ap_lda <- LDA(AssociatedPress, k = 2, control = list(seed = 123))

Podemos visualizar assim as palavras mais associadas a cada tópico:

terms(ap_lda, 10)

##       Topic 1   Topic 2     
##  [1,] "percent" "i"         
##  [2,] "million" "president" 
##  [3,] "new"     "government"
##  [4,] "year"    "people"    
##  [5,] "billion" "soviet"    
##  [6,] "last"    "new"       
##  [7,] "two"     "bush"      
##  [8,] "company" "two"       
##  [9,] "people"  "years"     
## [10,] "market"  "states"

Assim como quais os tópicos mais provavéis para cada documento:

head(topics(ap_lda), 20)

##  [1] 2 2 1 2 2 2 1 2 1 2 1 2 2 2 1 1 2 2 1 1

9.2.1.1.1.1 Word-topic probabilities

O pacote tidytext permite facilmente extrair as probabilidades \(\beta\) de cada palavra por tópico, ou seja a probabilidade de que o termos seja gerado a partir de cada tópico.

ap_topics <- tidy(ap_lda, matrix = "beta")

topic	term	beta
1	aaron	0.0000000
2	aaron	0.0000390
1	abandon	0.0000265
2	abandon	0.0000399
1	abandoned	0.0001391
2	abandoned	0.0000588
1	abandoning	0.0000000
2	abandoning	0.0000234
1	abbott	0.0000021
2	abbott	0.0000297
1	abboud	0.0000447
2	abboud	0.0000000
1	abc	0.0003646
2	abc	0.0000026
1	abcs	0.0000924
2	abcs	0.0000095
1	abctvs	0.0000075
2	abctvs	0.0000221
1	abdomen	0.0000359
2	abdomen	0.0000100
1	abducted	0.0000000
2	abducted	0.0000351
1	abduction	0.0000282
2	abduction	0.0000154
1	abductors	0.0000000
2	abductors	0.0000234
1	abdul	0.0000218
2	abdul	0.0000082
1	abide	0.0000000
2	abide	0.0000234
1	abilities	0.0000250
2	abilities	0.0000098
1	ability	0.0001038
2	ability	0.0001418
1	ablaze	0.0000508
2	ablaze	0.0000152
1	able	0.0004080
2	able	0.0003152
1	abm	0.0000000
2	abm	0.0000273
1	aboard	0.0004014
2	aboard	0.0000354
1	abolished	0.0000006
2	abolished	0.0000346
1	abortion	0.0000000
2	abortion	0.0002922
1	abortions	0.0000000
2	abortions	0.0000779
1	abraham	0.0000000
2	abraham	0.0000390
1	abrams	0.0000000
2	abrams	0.0000662
1	abroad	0.0000388
2	abroad	0.0001911
1	abrupt	0.0000180
2	abrupt	0.0000108
1	absence	0.0000473
2	absence	0.0000293
1	absent	0.0000277
2	absent	0.0000157
1	absolute	0.0000420
2	absolute	0.0000174
1	absolutely	0.0000503
2	absolutely	0.0001051
1	absorbed	0.0000351
2	absorbed	0.0000067
1	abu	0.0000162
2	abu	0.0000354
1	abuse	0.0000614
2	abuse	0.0001364
1	abused	0.0000000
2	abused	0.0000584
1	abuses	0.0000000
2	abuses	0.0001286
1	abyss	0.0000000
2	abyss	0.0000234
1	academic	0.0000165
2	academic	0.0000353
1	academics	0.0000000
2	academics	0.0000312
1	academy	0.0000002
2	academy	0.0002180
1	accelerated	0.0000272
2	accelerated	0.0000044
1	accept	0.0000289
2	accept	0.0002603
1	acceptable	0.0000008
2	acceptable	0.0000579
1	acceptance	0.0000350
2	acceptance	0.0000535
1	accepted	0.0000217
2	accepted	0.0002264
1	accepting	0.0000000
2	accepting	0.0000818
1	accepts	0.0000000
2	accepts	0.0000234
1	access	0.0001017
2	access	0.0002290
1	accessible	0.0000391
2	accessible	0.0000000
1	accident	0.0007256
2	accident	0.0000000
1	accidentally	0.0000614
2	accidentally	0.0000000
1	accidents	0.0002679
2	accidents	0.0000000
1	accommodate	0.0000093
2	accommodate	0.0000247
1	accompanied	0.0000714
2	accompanied	0.0000631
1	accompanying	0.0000447
2	accompanying	0.0000000
1	accomplish	0.0000000
2	accomplish	0.0000273
1	accomplished	0.0000163
2	accomplished	0.0000198
1	accomplishments	0.0000011
2	accomplishments	0.0000304
1	accord	0.0000393
2	accord	0.0001947
1	account	0.0002477
2	account	0.0001193
1	accountable	0.0000100
2	accountable	0.0000242
1	accountant	0.0000104
2	accountant	0.0000161
1	accounted	0.0000893
2	accounted	0.0000000
1	accounting	0.0001806
2	accounting	0.0000688
1	accounts	0.0002337
2	accounts	0.0001369
1	accurate	0.0000397
2	accurate	0.0000463
1	accusation	0.0000000
2	accusation	0.0000312
1	accusations	0.0000005
2	accusations	0.0000698
1	accuse	0.0000000
2	accuse	0.0000506
1	accused	0.0000006
2	accused	0.0008411
1	accuses	0.0000000
2	accuses	0.0000351
1	accusing	0.0000001
2	accusing	0.0000857
1	achieve	0.0000291
2	achieve	0.0000654
1	achieved	0.0000147
2	achieved	0.0000599
1	achieving	0.0000130
2	achieving	0.0000182
1	acid	0.0000378
2	acid	0.0000554
1	ackerman	0.0000000
2	ackerman	0.0000390
1	acknowledge	0.0000391
2	acknowledge	0.0000117
1	acknowledged	0.0001315
2	acknowledged	0.0002238
1	acknowledges	0.0000000
2	acknowledges	0.0000234
1	acknowledging	0.0000059
2	acknowledging	0.0000231
1	acquire	0.0001898
2	acquire	0.0000000
1	acquired	0.0002342
2	acquired	0.0000002
1	acquiring	0.0000693
2	acquiring	0.0000062
1	acquisition	0.0002764
2	acquisition	0.0000019
1	acquisitions	0.0000609
2	acquisitions	0.0000003
1	acquitted	0.0000000
2	acquitted	0.0000584
1	acre	0.0002455
2	acre	0.0000001
1	acreage	0.0000447
2	acreage	0.0000000
1	acres	0.0003014
2	acres	0.0000000
1	act	0.0000626
2	act	0.0004900
1	acted	0.0000154
2	acted	0.0001373
1	acting	0.0000751
2	acting	0.0001775
1	action	0.0002000
2	action	0.0007175
1	actions	0.0000533
2	actions	0.0002511
1	active	0.0002033
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1	historic	0.0000367
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1	historical	0.0000570
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1	history	0.0001880
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1	hits	0.0000837
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1	hitting	0.0000678
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1	holdings	0.0001724
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1	holocaust	0.0000000
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1	holy	0.0000014
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1	homosexuality	0.0000000
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1	homosexuals	0.0000000
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1	hostages	0.0000711
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1	hotels	0.0000599
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1	houphouetboigny	0.0000000
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1	hours	0.0009271
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1	households	0.0000270
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1	hull	0.0000338
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1	stickers	0.0000000
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1	stopping	0.0000000
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1	storer	0.0000000
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1	strategies	0.0000423
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1	strategists	0.0000297
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1	strategy	0.0000615
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1	streeters	0.0000502
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1	streets	0.0002089
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1	strength	0.0002929
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1	wanting	0.0000293
2	wanting	0.0000185
1	wants	0.0000847
2	wants	0.0004396
1	war	0.0001364
2	war	0.0017710
1	ward	0.0000666
2	ward	0.0000704
1	warehouse	0.0000819
2	warehouse	0.0000169
1	warehouses	0.0000420
2	warehouses	0.0000019
1	waren	0.0000000
2	waren	0.0000234
1	warfare	0.0000100
2	warfare	0.0000359
1	warheads	0.0000000
2	warheads	0.0000468
1	warm	0.0000988
2	warm	0.0000207
1	warming	0.0000216
2	warming	0.0000784
1	warmus	0.0000000
2	warmus	0.0000662
1	warn	0.0000265
2	warn	0.0000205
1	warned	0.0001103
2	warned	0.0002853
1	warner	0.0000010
2	warner	0.0000928
1	warning	0.0002467
2	warning	0.0000927
1	warnings	0.0001429
2	warnings	0.0000288
1	warrant	0.0000082
2	warrant	0.0001229
1	warranted	0.0000223
2	warranted	0.0000117
1	warrants	0.0000828
2	warrants	0.0000357
1	warren	0.0000842
2	warren	0.0000776
1	warring	0.0000115
2	warring	0.0000154
1	wars	0.0000208
2	wars	0.0001647
1	warsaw	0.0000001
2	warsaw	0.0001207
1	warships	0.0000793
2	warships	0.0000109
1	wartime	0.0000068
2	wartime	0.0000303
1	wary	0.0000097
2	wary	0.0000166
1	wash	0.0000587
2	wash	0.0000253
1	washed	0.0000558
2	washed	0.0000000
1	washington	0.0005220
2	washington	0.0011823
1	washingtonbased	0.0000610
2	washingtonbased	0.0000120
1	washingtons	0.0000062
2	washingtons	0.0000308
1	wasnt	0.0002058
2	wasnt	0.0001797
1	waste	0.0002865
2	waste	0.0001390
1	wastes	0.0000549
2	wastes	0.0000007
1	watch	0.0000860
2	watch	0.0001308
1	watched	0.0000907
2	watched	0.0000497
1	watchers	0.0000237
2	watchers	0.0000068
1	watches	0.0000335
2	watches	0.0000000
1	watching	0.0000967
2	watching	0.0001156
1	water	0.0012944
2	water	0.0000004
1	watergate	0.0000000
2	watergate	0.0000351
1	waters	0.0001674
2	waters	0.0000000
1	waterway	0.0000278
2	waterway	0.0000312
1	watkins	0.0000824
2	watkins	0.0000476
1	watson	0.0000335
2	watson	0.0000000
1	wave	0.0001158
2	wave	0.0000478
1	waved	0.0000000
2	waved	0.0000390
1	waves	0.0000628
2	waves	0.0000068
1	waving	0.0000068
2	waving	0.0000264
1	wayne	0.0000463
2	wayne	0.0000261
1	ways	0.0001343
2	ways	0.0002257
1	weak	0.0001847
2	weak	0.0000113
1	weaken	0.0000158
2	weaken	0.0000241
1	weakened	0.0000604
2	weakened	0.0000162
1	weakening	0.0000670
2	weakening	0.0000000
1	weaker	0.0001005
2	weaker	0.0000000
1	weakness	0.0001846
2	weakness	0.0000114
1	wealth	0.0000000
2	wealth	0.0000662
1	wealthy	0.0000219
2	wealthy	0.0001133
1	weapon	0.0000349
2	weapon	0.0001276
1	weaponry	0.0000001
2	weaponry	0.0000350
1	weapons	0.0001440
2	weapons	0.0004877
1	wear	0.0000265
2	wear	0.0000828
1	wearing	0.0000504
2	wearing	0.0000700
1	weather	0.0006530
2	weather	0.0000000
1	webb	0.0000000
2	webb	0.0000701
1	webster	0.0000006
2	webster	0.0000346
1	wed	0.0000226
2	wed	0.0000465
1	wedding	0.0000159
2	wedding	0.0000707
1	wednesday	0.0016056
2	wednesday	0.0014896
1	wednesdays	0.0001089
2	wednesdays	0.0000370
1	wedtech	0.0001116
2	wedtech	0.0000000
1	week	0.0018469
2	week	0.0014497
1	weekend	0.0002394
2	weekend	0.0001874
1	weekends	0.0000000
2	weekends	0.0000234
1	weeklong	0.0000012
2	weeklong	0.0000342
1	weekly	0.0001101
2	weekly	0.0001179
1	weekold	0.0000265
2	weekold	0.0000127
1	weeks	0.0006573
2	weeks	0.0006750
1	weicker	0.0000000
2	weicker	0.0000429
1	weigh	0.0000272
2	weigh	0.0000160
1	weighing	0.0000653
2	weighing	0.0000128
1	weight	0.0001750
2	weight	0.0000259
1	weinstein	0.0000000
2	weinstein	0.0000506
1	welcome	0.0000168
2	welcome	0.0001441
1	welcomed	0.0000110
2	welcomed	0.0000936
1	welcoming	0.0000069
2	welcoming	0.0000186
1	welfare	0.0000000
2	welfare	0.0001403
1	wellknown	0.0000181
2	wellknown	0.0000341
1	wells	0.0001005
2	wells	0.0000000
1	wellstone	0.0000000
2	wellstone	0.0000351
1	wellwishers	0.0000174
2	wellwishers	0.0000151
1	went	0.0006551
2	went	0.0005518
1	werent	0.0000441
2	werent	0.0000510
1	west	0.0012392
2	west	0.0012428
1	western	0.0003965
2	western	0.0004791
1	westerners	0.0000000
2	westerners	0.0000545
1	westernstyle	0.0000000
2	westernstyle	0.0000468
1	westinghouse	0.0000614
2	westinghouse	0.0000000
1	wet	0.0000668
2	wet	0.0000040
1	weve	0.0002723
2	weve	0.0002658
1	whales	0.0000000
2	whales	0.0000273
1	whats	0.0000773
2	whats	0.0001058
1	wheat	0.0003349
2	wheat	0.0000000
1	wheel	0.0000434
2	wheel	0.0000009
1	wheelchair	0.0000000
2	wheelchair	0.0000234
1	wheelchairs	0.0000142
2	wheelchairs	0.0000291
1	whereabouts	0.0000006
2	whereabouts	0.0000229
1	whip	0.0000000
2	whip	0.0000623
1	whirlpool	0.0000558
2	whirlpool	0.0000000
1	whistleblowers	0.0000000
2	whistleblowers	0.0000312
1	white	0.0002070
2	white	0.0014801
1	whites	0.0000084
2	whites	0.0001811
1	wholesale	0.0001061
2	wholesale	0.0000000
1	whos	0.0000471
2	whos	0.0000334
1	wickes	0.0000000
2	wickes	0.0000312
1	wide	0.0002120
2	wide	0.0000429
1	widely	0.0001650
2	widely	0.0001108
1	wider	0.0000212
2	wider	0.0000241
1	widespread	0.0001161
2	widespread	0.0001527
1	widow	0.0000145
2	widow	0.0000522
1	wiese	0.0000502
2	wiese	0.0000000
1	wife	0.0001431
2	wife	0.0007884
1	wifes	0.0000004
2	wifes	0.0000465
1	wilbury	0.0000000
2	wilbury	0.0000273
1	wilburys	0.0000000
2	wilburys	0.0000351
1	wild	0.0001660
2	wild	0.0000010
1	wilder	0.0000002
2	wilder	0.0000349
1	wilderness	0.0000280
2	wilderness	0.0000077
1	wildlife	0.0001898
2	wildlife	0.0000000
1	wildly	0.0000147
2	wildly	0.0000170
1	william	0.0003531
2	william	0.0003769
1	williams	0.0000698
2	williams	0.0002785
1	williamson	0.0000330
2	williamson	0.0000004
1	willie	0.0000003
2	willie	0.0000387
1	willing	0.0001145
2	willing	0.0001577
1	willingness	0.0000072
2	willingness	0.0000378
1	willis	0.0000000
2	willis	0.0000273
1	wilshire	0.0000558
2	wilshire	0.0000000
1	wilson	0.0001675
2	wilson	0.0000779
1	win	0.0000704
2	win	0.0004924
1	wind	0.0002184
2	wind	0.0000150
1	windfall	0.0000174
2	windfall	0.0000307
1	window	0.0000649
2	window	0.0000093
1	windows	0.0001730
2	windows	0.0000000
1	winds	0.0003448
2	winds	0.0000048
1	windy	0.0000335
2	windy	0.0000000
1	wine	0.0002769
2	wine	0.0000054
1	winery	0.0000335
2	winery	0.0000000
1	wines	0.0000558
2	wines	0.0000000
1	wing	0.0000950
2	wing	0.0000817
1	wings	0.0001046
2	wings	0.0000166
1	winner	0.0000256
2	winner	0.0001380
1	winners	0.0000251
2	winners	0.0000604
1	winning	0.0000988
2	winning	0.0001180
1	wins	0.0000000
2	wins	0.0000662
1	winter	0.0002925
2	winter	0.0000530
1	wipe	0.0000382
2	wipe	0.0000123
1	wiped	0.0000406
2	wiped	0.0000106
1	wire	0.0000224
2	wire	0.0000818
1	wis	0.0001283
2	wis	0.0000000
1	wisconsin	0.0000677
2	wisconsin	0.0000891
1	wish	0.0000137
2	wish	0.0001229
1	wished	0.0000246
2	wished	0.0000179
1	wishes	0.0000165
2	wishes	0.0000274
1	withdraw	0.0000188
2	withdraw	0.0001583
1	withdrawal	0.0000000
2	withdrawal	0.0002026
1	withdrawing	0.0000000
2	withdrawing	0.0000467
1	withdrawn	0.0000357
2	withdrawn	0.0000374
1	withdrew	0.0000462
2	withdrew	0.0000457
1	withheld	0.0000277
2	withheld	0.0000664
1	withhold	0.0000000
2	withhold	0.0000234
1	witness	0.0000332
2	witness	0.0001366
1	witnessed	0.0000003
2	witnessed	0.0000271
1	witnesses	0.0001116
2	witnesses	0.0002806
1	witter	0.0000614
2	witter	0.0000000
1	wittman	0.0000334
2	wittman	0.0000001
1	wives	0.0000000
2	wives	0.0000390
1	woes	0.0000250
2	woes	0.0000176
1	wolf	0.0000341
2	wolf	0.0000151
1	wolvovitz	0.0000000
2	wolvovitz	0.0000273
1	woman	0.0001767
2	woman	0.0004688
1	womans	0.0000179
2	womans	0.0000771
1	womb	0.0000040
2	womb	0.0000206
1	women	0.0002283
2	women	0.0009159
1	womens	0.0000273
2	womens	0.0001212
1	won	0.0000922
2	won	0.0007304
1	wonder	0.0000532
2	wonder	0.0000408
1	wonderful	0.0000163
2	wonderful	0.0000276
1	wondering	0.0000110
2	wondering	0.0000157
1	wont	0.0002114
2	wont	0.0002187
1	wood	0.0001716
2	wood	0.0000244
1	wooden	0.0000850
2	wooden	0.0000030
1	woods	0.0000497
2	woods	0.0000666
1	woody	0.0000130
2	woody	0.0000221
1	word	0.0001725
2	word	0.0002146
1	words	0.0000230
2	words	0.0001905
1	wore	0.0000148
2	wore	0.0000403
1	work	0.0011933
2	work	0.0010644
1	worked	0.0003084
2	worked	0.0003886
1	worker	0.0002348
2	worker	0.0000738
1	workers	0.0017661
2	workers	0.0004542
1	workforce	0.0000391
2	workforce	0.0000000
1	working	0.0004273
2	working	0.0004926
1	workplace	0.0000000
2	workplace	0.0000390
1	works	0.0002413
2	works	0.0002017
1	world	0.0006327
2	world	0.0015804
1	worlds	0.0003672
2	worlds	0.0000749
1	worldwide	0.0002183
2	worldwide	0.0000347
1	worn	0.0000000
2	worn	0.0000273
1	worried	0.0000914
2	worried	0.0000882
1	worries	0.0001389
2	worries	0.0000121
1	worry	0.0000877
2	worry	0.0000634
1	worse	0.0001427
2	worse	0.0000485
1	worsened	0.0000335
2	worsened	0.0000000
1	worsening	0.0000271
2	worsening	0.0000044
1	worst	0.0002291
2	worst	0.0000738
1	worth	0.0004831
2	worth	0.0000875
1	worthless	0.0000117
2	worthless	0.0000191
1	wouldbe	0.0000050
2	wouldbe	0.0000315
1	wouldnt	0.0001198
2	wouldnt	0.0002164
1	wound	0.0000250
2	wound	0.0000721
1	wounded	0.0002646
2	wounded	0.0002634
1	wounding	0.0000635
2	wounding	0.0000648
1	wounds	0.0000412
2	wounds	0.0000492
1	wppss	0.0000614
2	wppss	0.0000000
1	wrangling	0.0000000
2	wrangling	0.0000234
1	wrapped	0.0000224
2	wrapped	0.0000350
1	wrath	0.0000134
2	wrath	0.0000140
1	wright	0.0000915
2	wright	0.0001426
1	write	0.0000219
2	write	0.0001094
1	writer	0.0000090
2	writer	0.0001106
1	writers	0.0000004
2	writers	0.0000854
1	writes	0.0000003
2	writes	0.0000426
1	writing	0.0000070
2	writing	0.0001899
1	written	0.0001004
2	written	0.0002689
1	wrong	0.0001006
2	wrong	0.0002259
1	wrongdoing	0.0000000
2	wrongdoing	0.0001052
1	wrote	0.0001122
2	wrote	0.0003970
1	wte	0.0000335
2	wte	0.0000000
1	wva	0.0000305
2	wva	0.0000021
1	wynberg	0.0000000
2	wynberg	0.0000468
1	wyo	0.0000272
2	wyo	0.0000044
1	wyoming	0.0000490
2	wyoming	0.0000282
1	x	0.0000949
2	x	0.0000000
1	xhosas	0.0000000
2	xhosas	0.0000273
1	xinhua	0.0000068
2	xinhua	0.0000303
1	y	0.0000117
2	y	0.0000464
1	yale	0.0000000
2	yale	0.0000701
1	yancy	0.0000335
2	yancy	0.0000000
1	yankee	0.0000084
2	yankee	0.0000175
1	yankees	0.0000000
2	yankees	0.0000468
1	yard	0.0000708
2	yard	0.0001220
1	yards	0.0001760
2	yards	0.0000058
1	yasser	0.0000000
2	yasser	0.0000429
1	yates	0.0000000
2	yates	0.0000740
1	year	0.0057502
2	year	0.0021264
1	yearearlier	0.0000391
2	yearearlier	0.0000000
1	yearend	0.0000305
2	yearend	0.0000021
1	yearlong	0.0000167
2	yearlong	0.0000117
1	yearly	0.0000495
2	yearly	0.0000005
1	yearold	0.0005665
2	yearold	0.0011551
1	years	0.0026432
2	years	0.0033873
1	yegor	0.0000000
2	yegor	0.0000273
1	yelled	0.0000001
2	yelled	0.0000389
1	yellow	0.0001080
2	yellow	0.0000337
1	yellowstone	0.0000670
2	yellowstone	0.0000000
1	yeltsin	0.0000000
2	yeltsin	0.0001480
1	yen	0.0010661
2	yen	0.0000000
1	yesterday	0.0000210
2	yesterday	0.0000360
1	yeutter	0.0000014
2	yeutter	0.0001510
1	yield	0.0002233
2	yield	0.0000000
1	yielded	0.0000270
2	yielded	0.0000162
1	yields	0.0001786
2	yields	0.0000000
1	yitzhak	0.0000000
2	yitzhak	0.0000662
1	york	0.0023544
2	york	0.0007488
1	yorkbased	0.0000413
2	yorkbased	0.0000296
1	yorkers	0.0000128
2	yorkers	0.0000145
1	yorks	0.0001112
2	yorks	0.0000431
1	yosemite	0.0000893
2	yosemite	0.0000000
1	youll	0.0000287
2	youll	0.0000423
1	young	0.0003451
2	young	0.0004175
1	younger	0.0000337
2	younger	0.0000894
1	youngest	0.0000188
2	youngest	0.0000258
1	youngsters	0.0000150
2	youngsters	0.0000402
1	youre	0.0001382
2	youre	0.0002035
1	youth	0.0000021
2	youth	0.0000842
1	youths	0.0000231
2	youths	0.0001280
1	youve	0.0000782
2	youve	0.0000857
1	yugoslav	0.0000168
2	yugoslav	0.0000194
1	yugoslavia	0.0000256
2	yugoslavia	0.0000406
1	yuri	0.0000000
2	yuri	0.0000312
1	zaccaro	0.0000000
2	zaccaro	0.0000506
1	zaire	0.0000000
2	zaire	0.0000273
1	zambia	0.0000000
2	zambia	0.0000390
1	zealand	0.0000220
2	zealand	0.0000197
1	zennoh	0.0000335
2	zennoh	0.0000000
1	zero	0.0000837
2	zero	0.0000117
1	zieman	0.0000000
2	zieman	0.0000234
1	ziemet	0.0000391
2	ziemet	0.0000000
1	zimbabwe	0.0000001
2	zimbabwe	0.0000467
1	zinoviev	0.0000000
2	zinoviev	0.0000234
1	zone	0.0000367
2	zone	0.0000289
1	zones	0.0000007
2	zones	0.0000580
1	zoo	0.0000502
2	zoo	0.0000000
1	zubal	0.0000558
2	zubal	0.0000000
1	zurich	0.0001340
2	zurich	0.0000000

Podemos usar esse resultado para visualizar os principais termos que compõem cada tópico:

ap_top_terms <- ap_topics %>%
  group_by(topic) %>%
  top_n(10, beta) %>%
  ungroup() %>%
  arrange(topic, -beta)

ap_top_terms %>%
  mutate(term = reorder(term, beta)) %>%
  ggplot(aes(term, beta, fill = factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  coord_flip()

O gráfico permite analisar os dois tópicos. As palavras mais comuns no tópico 1 sugerem que ele pode representa notícias na área da economia/finanças. No tópico 2 as palavras mais comuns sugerem que este tópico representa notícias sobre política. É importante constatar que algumas palavras são comuns nos dois tópicos. Essa é uma vantagem da modelagem de tópico, em oposição aos métodos de “hard clustering”: os tópicos obtidos podem ter alguma sobreposição de palavras.

Uma alternativa a essa abordagem é analisar palavras que apresentam as maiores diferenças entre os dois tópicos:

beta_spread <- ap_topics %>%
  mutate(topic = paste0("topic", topic)) %>%
  spread(topic, beta) %>%
  filter(topic1 > .001 | topic2 > .001) %>%
  mutate(log_ratio = log2(topic2 / topic1))

beta_spread %>%
  group_by(direction = log_ratio > 0) %>%
  top_n(10, abs(log_ratio)) %>%
  ungroup() %>%
  mutate(term = reorder(term, log_ratio)) %>%
  ggplot(aes(term, log_ratio)) +
    geom_col() +
  labs(y = "Razão logarítmica de beta no tópico 2 / tópico 1") +
  coord_flip()

Os termos mais comuns no tópico 2 em relação ao tópico 1 incluem “democratic” e “republican”. O tópico 1 foi mais caracterizado por termos referentes a moedas como “yen” e “dollar”, além de termos financeiros como “index”, “prices” e “rates”. Isso ajuda a confirmar que os dois tópicos que o algoritmo identificou referem-se a notícias políticas e financeiras, respectivamente.

9.2.1.1.1.2 Document-topic probabilities

Podemos examinar as probabilidades por-documento-por-tópico, \(\gamma\) (Gamma).

ap_documents <- tidy(ap_lda, matrix = "gamma") %>% arrange(document)

document	topic	gamma
1	1	0.2480617
1	2	0.7519383
2	1	0.3615485
2	2	0.6384515
3	1	0.5265844
3	2	0.4734156
4	1	0.3566530
4	2	0.6433470
5	1	0.1812767
5	2	0.8187233
6	1	0.0005883
6	2	0.9994117
7	1	0.7734216
7	2	0.2265784
8	1	0.0044517
8	2	0.9955483
9	1	0.9669915
9	2	0.0330085
10	1	0.1468905
10	2	0.8531095
11	1	0.9949971
11	2	0.0050029
12	1	0.3943949
12	2	0.6056051
13	1	0.0015357
13	2	0.9984643
14	1	0.0085740
14	2	0.9914260
15	1	0.9991472
15	2	0.0008528
16	1	0.7610596
16	2	0.2389404
17	1	0.1436260
17	2	0.8563740
18	1	0.0007197
18	2	0.9992803
19	1	0.9987216
19	2	0.0012784
20	1	0.9993127
20	2	0.0006873
21	1	0.9100520
21	2	0.0899480
22	1	0.9773016
22	2	0.0226984
23	1	0.9668815
23	2	0.0331185
24	1	0.1099811
24	2	0.8900189
25	1	0.8627618
25	2	0.1372382
26	1	0.5162087
26	2	0.4837913
27	1	0.0009150
27	2	0.9990850
28	1	0.7011853
28	2	0.2988147
29	1	0.0062584
29	2	0.9937416
30	1	0.9582044
30	2	0.0417956
31	1	0.9923313
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1813	1	0.3666350
1813	2	0.6333650
1814	1	0.1320311
1814	2	0.8679689
1815	1	0.0964078
1815	2	0.9035922
1816	1	0.0019328
1816	2	0.9980672
1817	1	0.5085482
1817	2	0.4914518
1818	1	0.9987142
1818	2	0.0012858
1819	1	0.0019469
1819	2	0.9980531
1820	1	0.0009167
1820	2	0.9990833
1821	1	0.0010746
1821	2	0.9989254
1822	1	0.5538550
1822	2	0.4461450
1823	1	0.1021830
1823	2	0.8978170
1824	1	0.3355228
1824	2	0.6644772
1825	1	0.1177484
1825	2	0.8822516
1826	1	0.1545641
1826	2	0.8454359
1827	1	0.2785764
1827	2	0.7214236
1828	1	0.0403128
1828	2	0.9596872
1829	1	0.1410082
1829	2	0.8589918
1830	1	0.4338702
1830	2	0.5661298
1831	1	0.1123871
1831	2	0.8876129
1832	1	0.0534114
1832	2	0.9465886
1833	1	0.9993319
1833	2	0.0006681
1834	1	0.7532587
1834	2	0.2467413
1835	1	0.9979615
1835	2	0.0020385
1836	1	0.9975570
1836	2	0.0024430
1837	1	0.9983436
1837	2	0.0016564
1838	1	0.2012041
1838	2	0.7987959
1839	1	0.5323314
1839	2	0.4676686
1840	1	0.0011078
1840	2	0.9988922
1841	1	0.6808508
1841	2	0.3191492
1842	1	0.9970335
1842	2	0.0029665
1843	1	0.2450200
1843	2	0.7549800
1844	1	0.7012178
1844	2	0.2987822
1845	1	0.8033136
1845	2	0.1966864
1846	1	0.1860599
1846	2	0.8139401
1847	1	0.0020351
1847	2	0.9979649
1848	1	0.0011324
1848	2	0.9988676
1849	1	0.0019663
1849	2	0.9980337
1850	1	0.5072935
1850	2	0.4927065
1851	1	0.0088000
1851	2	0.9912000
1852	1	0.2242799
1852	2	0.7757201
1853	1	0.2652620
1853	2	0.7347380
1854	1	0.5331003
1854	2	0.4668997
1855	1	0.4877630
1855	2	0.5122370
1856	1	0.6024019
1856	2	0.3975981
1857	1	0.9986272
1857	2	0.0013728
1858	1	0.7459236
1858	2	0.2540764
1859	1	0.0033228
1859	2	0.9966772
1860	1	0.2325961
1860	2	0.7674039
1861	1	0.9986068
1861	2	0.0013932
1862	1	0.9982250
1862	2	0.0017750
1863	1	0.9483077
1863	2	0.0516923
1864	1	0.0011201
1864	2	0.9988799
1865	1	0.9716343
1865	2	0.0283657
1866	1	0.1532923
1866	2	0.8467077
1867	1	0.8872075
1867	2	0.1127925
1868	1	0.0014418
1868	2	0.9985582
1869	1	0.9993200
1869	2	0.0006800
1870	1	0.0009756
1870	2	0.9990244
1871	1	0.0756817
1871	2	0.9243183
1872	1	0.9975512
1872	2	0.0024488
1873	1	0.0900286
1873	2	0.9099714
1874	1	0.1792984
1874	2	0.8207016
1875	1	0.7145536
1875	2	0.2854464
1876	1	0.8515402
1876	2	0.1484598
1877	1	0.9977247
1877	2	0.0022753
1878	1	0.7561325
1878	2	0.2438675
1879	1	0.1770664
1879	2	0.8229336
1880	1	0.2606003
1880	2	0.7393997
1881	1	0.8255102
1881	2	0.1744898
1882	1	0.6509563
1882	2	0.3490437
1883	1	0.6199081
1883	2	0.3800919
1884	1	0.7112824
1884	2	0.2887176
1885	1	0.0009788
1885	2	0.9990212
1886	1	0.8958532
1886	2	0.1041468
1887	1	0.5688753
1887	2	0.4311247
1888	1	0.6958494
1888	2	0.3041506
1889	1	0.0031338
1889	2	0.9968662
1890	1	0.5678974
1890	2	0.4321026
1891	1	0.2159254
1891	2	0.7840746
1892	1	0.0009530
1892	2	0.9990470
1893	1	0.0005803
1893	2	0.9994197
1894	1	0.0004988
1894	2	0.9995012
1895	1	0.1548299
1895	2	0.8451701
1896	1	0.4680394
1896	2	0.5319606
1897	1	0.6814792
1897	2	0.3185208
1898	1	0.0014361
1898	2	0.9985639
1899	1	0.9962685
1899	2	0.0037315
1900	1	0.3819415
1900	2	0.6180585
1901	1	0.5273892
1901	2	0.4726108
1902	1	0.9987437
1902	2	0.0012563
1903	1	0.0038761
1903	2	0.9961239
1904	1	0.9977838
1904	2	0.0022162
1905	1	0.4058112
1905	2	0.5941888
1906	1	0.0017676
1906	2	0.9982324
1907	1	0.0018695
1907	2	0.9981305
1908	1	0.5441868
1908	2	0.4558132
1909	1	0.5780873
1909	2	0.4219127
1910	1	0.1358530
1910	2	0.8641470
1911	1	0.9676276
1911	2	0.0323724
1912	1	0.7871126
1912	2	0.2128874
1913	1	0.7437638
1913	2	0.2562362
1914	1	0.9991413
1914	2	0.0008587
1915	1	0.0008212
1915	2	0.9991788
1916	1	0.0006712
1916	2	0.9993288
1917	1	0.0005323
1917	2	0.9994677
1918	1	0.3371551
1918	2	0.6628449
1919	1	0.7890812
1919	2	0.2109188
1920	1	0.0296235
1920	2	0.9703765
1921	1	0.0008259
1921	2	0.9991741
1922	1	0.1278964
1922	2	0.8721036
1923	1	0.9989367
1923	2	0.0010633
1924	1	0.0246034
1924	2	0.9753966
1925	1	0.0009123
1925	2	0.9990877
1926	1	0.9985426
1926	2	0.0014574
1927	1	0.0484455
1927	2	0.9515545
1928	1	0.0653388
1928	2	0.9346612
1929	1	0.9772891
1929	2	0.0227109
1930	1	0.8678843
1930	2	0.1321157
1931	1	0.9989486
1931	2	0.0010514
1932	1	0.9990359
1932	2	0.0009641
1933	1	0.5188593
1933	2	0.4811407
1934	1	0.2001939
1934	2	0.7998061
1935	1	0.7157389
1935	2	0.2842611
1936	1	0.2578041
1936	2	0.7421959
1937	1	0.4839569
1937	2	0.5160431
1938	1	0.6857143
1938	2	0.3142857
1939	1	0.7451567
1939	2	0.2548433
1940	1	0.0015239
1940	2	0.9984761
1941	1	0.0708497
1941	2	0.9291503
1942	1	0.9968116
1942	2	0.0031884
1943	1	0.9854037
1943	2	0.0145963
1944	1	0.7082038
1944	2	0.2917962
1945	1	0.1625454
1945	2	0.8374546
1946	1	0.7414403
1946	2	0.2585597
1947	1	0.9969895
1947	2	0.0030105
1948	1	0.0618257
1948	2	0.9381743
1949	1	0.9969470
1949	2	0.0030530
1950	1	0.6354512
1950	2	0.3645488
1951	1	0.7004215
1951	2	0.2995785
1952	1	0.9975881
1952	2	0.0024119
1953	1	0.0016093
1953	2	0.9983907
1954	1	0.0036917
1954	2	0.9963083
1955	1	0.0031101
1955	2	0.9968899
1956	1	0.0999149
1956	2	0.9000851
1957	1	0.9989907
1957	2	0.0010093
1958	1	0.5011034
1958	2	0.4988966
1959	1	0.0040300
1959	2	0.9959700
1960	1	0.0005298
1960	2	0.9994702
1961	1	0.3003543
1961	2	0.6996457
1962	1	0.9975032
1962	2	0.0024968
1963	1	0.2406276
1963	2	0.7593724
1964	1	0.0430737
1964	2	0.9569263
1965	1	0.9967696
1965	2	0.0032304
1966	1	0.9884477
1966	2	0.0115523
1967	1	0.1776851
1967	2	0.8223149
1968	1	0.8387075
1968	2	0.1612925
1969	1	0.9964061
1969	2	0.0035939
1970	1	0.0010798
1970	2	0.9989202
1971	1	0.9958754
1971	2	0.0041246
1972	1	0.0509160
1972	2	0.9490840
1973	1	0.1830954
1973	2	0.8169046
1974	1	0.9190994
1974	2	0.0809006
1975	1	0.9988519
1975	2	0.0011481
1976	1	0.2653502
1976	2	0.7346498
1977	1	0.2711980
1977	2	0.7288020
1978	1	0.5513813
1978	2	0.4486187
1979	1	0.3816186
1979	2	0.6183814
1980	1	0.0492664
1980	2	0.9507336
1981	1	0.3682350
1981	2	0.6317650
1982	1	0.0622813
1982	2	0.9377187
1983	1	0.0006935
1983	2	0.9993065
1984	1	0.2518257
1984	2	0.7481743
1985	1	0.0025831
1985	2	0.9974169
1986	1	0.9274625
1986	2	0.0725375
1987	1	0.1934048
1987	2	0.8065952
1988	1	0.6898785
1988	2	0.3101215
1989	1	0.8747910
1989	2	0.1252090
1990	1	0.0008667
1990	2	0.9991333
1991	1	0.7429168
1991	2	0.2570832
1992	1	0.0027638
1992	2	0.9972362
1993	1	0.9946773
1993	2	0.0053227
1994	1	0.1983231
1994	2	0.8016769
1995	1	0.8273388
1995	2	0.1726612
1996	1	0.9044263
1996	2	0.0955737
1997	1	0.8358943
1997	2	0.1641057
1998	1	0.7161074
1998	2	0.2838926
1999	1	0.3425451
1999	2	0.6574549
2000	1	0.0366877
2000	2	0.9633123
2001	1	0.0018693
2001	2	0.9981307
2002	1	0.1989716
2002	2	0.8010284
2003	1	0.4579488
2003	2	0.5420512
2004	1	0.9355794
2004	2	0.0644206
2005	1	0.5569705
2005	2	0.4430295
2006	1	0.9073128
2006	2	0.0926872
2007	1	0.4032143
2007	2	0.5967857
2008	1	0.4906151
2008	2	0.5093849
2009	1	0.0207946
2009	2	0.9792054
2010	1	0.9977985
2010	2	0.0022015
2011	1	0.7194619
2011	2	0.2805381
2012	1	0.8062987
2012	2	0.1937013
2013	1	0.2688356
2013	2	0.7311644
2014	1	0.7042710
2014	2	0.2957290
2015	1	0.8876681
2015	2	0.1123319
2016	1	0.0011951
2016	2	0.9988049
2017	1	0.3366084
2017	2	0.6633916
2018	1	0.0015170
2018	2	0.9984830
2019	1	0.9494978
2019	2	0.0505022
2020	1	0.1002517
2020	2	0.8997483
2021	1	0.3055490
2021	2	0.6944510
2022	1	0.9965621
2022	2	0.0034379
2023	1	0.0006445
2023	2	0.9993555
2024	1	0.9977667
2024	2	0.0022333
2025	1	0.9968968
2025	2	0.0031032
2026	1	0.3534577
2026	2	0.6465423
2027	1	0.5845897
2027	2	0.4154103
2028	1	0.9994624
2028	2	0.0005376
2029	1	0.0036699
2029	2	0.9963301
2030	1	0.0009039
2030	2	0.9990961
2031	1	0.9768534
2031	2	0.0231466
2032	1	0.0074511
2032	2	0.9925489
2033	1	0.9994814
2033	2	0.0005186
2034	1	0.3997124
2034	2	0.6002876
2035	1	0.0013464
2035	2	0.9986536
2036	1	0.5658165
2036	2	0.4341835
2037	1	0.3929659
2037	2	0.6070341
2038	1	0.0023639
2038	2	0.9976361
2039	1	0.1653724
2039	2	0.8346276
2040	1	0.1021729
2040	2	0.8978271
2041	1	0.7673295
2041	2	0.2326705
2042	1	0.1442902
2042	2	0.8557098
2043	1	0.9979134
2043	2	0.0020866
2044	1	0.7095875
2044	2	0.2904125
2045	1	0.0014733
2045	2	0.9985267
2046	1	0.0011090
2046	2	0.9988910
2047	1	0.9990315
2047	2	0.0009685
2048	1	0.4915645
2048	2	0.5084355
2049	1	0.0010345
2049	2	0.9989655
2050	1	0.8386236
2050	2	0.1613764
2051	1	0.0499899
2051	2	0.9500101
2052	1	0.0017027
2052	2	0.9982973
2053	1	0.0148088
2053	2	0.9851912
2054	1	0.9834046
2054	2	0.0165954
2055	1	0.0037989
2055	2	0.9962011
2056	1	0.9968778
2056	2	0.0031222
2057	1	0.0014759
2057	2	0.9985241
2058	1	0.7016103
2058	2	0.2983897
2059	1	0.0461718
2059	2	0.9538282
2060	1	0.1778209
2060	2	0.8221791
2061	1	0.2775227
2061	2	0.7224773
2062	1	0.3463987
2062	2	0.6536013
2063	1	0.9410214
2063	2	0.0589786
2064	1	0.6203421
2064	2	0.3796579
2065	1	0.0015469
2065	2	0.9984531
2066	1	0.1788012
2066	2	0.8211988
2067	1	0.0033668
2067	2	0.9966332
2068	1	0.7667242
2068	2	0.2332758
2069	1	0.0009967
2069	2	0.9990033
2070	1	0.1097034
2070	2	0.8902966
2071	1	0.2692324
2071	2	0.7307676
2072	1	0.9985662
2072	2	0.0014338
2073	1	0.0006762
2073	2	0.9993238
2074	1	0.1220063
2074	2	0.8779937
2075	1	0.9568500
2075	2	0.0431500
2076	1	0.2287728
2076	2	0.7712272
2077	1	0.0014751
2077	2	0.9985249
2078	1	0.9112473
2078	2	0.0887527
2079	1	0.0024073
2079	2	0.9975927
2080	1	0.3861386
2080	2	0.6138614
2081	1	0.0006202
2081	2	0.9993798
2082	1	0.7595131
2082	2	0.2404869
2083	1	0.9956332
2083	2	0.0043668
2084	1	0.7600395
2084	2	0.2399605
2085	1	0.1227023
2085	2	0.8772977
2086	1	0.9975828
2086	2	0.0024172
2087	1	0.0676150
2087	2	0.9323850
2088	1	0.0005937
2088	2	0.9994063
2089	1	0.5674981
2089	2	0.4325019
2090	1	0.9428495
2090	2	0.0571505
2091	1	0.0007605
2091	2	0.9992395
2092	1	0.0861380
2092	2	0.9138620
2093	1	0.2253558
2093	2	0.7746442
2094	1	0.5039165
2094	2	0.4960835
2095	1	0.0005357
2095	2	0.9994643
2096	1	0.5774561
2096	2	0.4225439
2097	1	0.9989204
2097	2	0.0010796
2098	1	0.0081503
2098	2	0.9918497
2099	1	0.9962758
2099	2	0.0037242
2100	1	0.0012992
2100	2	0.9987008
2101	1	0.0509889
2101	2	0.9490111
2102	1	0.2423356
2102	2	0.7576644
2103	1	0.2275970
2103	2	0.7724030
2104	1	0.4162695
2104	2	0.5837305
2105	1	0.6587155
2105	2	0.3412845
2106	1	0.0008615
2106	2	0.9991385
2107	1	0.9967032
2107	2	0.0032968
2108	1	0.3778932
2108	2	0.6221068
2109	1	0.1594041
2109	2	0.8405959
2110	1	0.7243414
2110	2	0.2756586
2111	1	0.2923460
2111	2	0.7076540
2112	1	0.9703128
2112	2	0.0296872
2113	1	0.1511871
2113	2	0.8488129
2114	1	0.1006747
2114	2	0.8993253
2115	1	0.5015087
2115	2	0.4984913
2116	1	0.3863880
2116	2	0.6136120
2117	1	0.2594812
2117	2	0.7405188
2118	1	0.6022616
2118	2	0.3977384
2119	1	0.9983856
2119	2	0.0016144
2120	1	0.0012099
2120	2	0.9987901
2121	1	0.3101571
2121	2	0.6898429
2122	1	0.0010778
2122	2	0.9989222
2123	1	0.7505358
2123	2	0.2494642
2124	1	0.7798271
2124	2	0.2201729
2125	1	0.3286850
2125	2	0.6713150
2126	1	0.0014264
2126	2	0.9985736
2127	1	0.0318965
2127	2	0.9681035
2128	1	0.0022867
2128	2	0.9977133
2129	1	0.9928669
2129	2	0.0071331
2130	1	0.9953436
2130	2	0.0046564
2131	1	0.0008670
2131	2	0.9991330
2132	1	0.2717404
2132	2	0.7282596
2133	1	0.9982361
2133	2	0.0017639
2134	1	0.4533618
2134	2	0.5466382
2135	1	0.2467627
2135	2	0.7532373
2136	1	0.5579563
2136	2	0.4420437
2137	1	0.1117956
2137	2	0.8882044
2138	1	0.9699554
2138	2	0.0300446
2139	1	0.0217532
2139	2	0.9782468
2140	1	0.2490154
2140	2	0.7509846
2141	1	0.4388072
2141	2	0.5611928
2142	1	0.8018381
2142	2	0.1981619
2143	1	0.0547699
2143	2	0.9452301
2144	1	0.2919778
2144	2	0.7080222
2145	1	0.9988611
2145	2	0.0011389
2146	1	0.6769461
2146	2	0.3230539
2147	1	0.0006821
2147	2	0.9993179
2148	1	0.9990600
2148	2	0.0009400
2149	1	0.9963880
2149	2	0.0036120
2150	1	0.2055449
2150	2	0.7944551
2151	1	0.9992445
2151	2	0.0007555
2152	1	0.0013990
2152	2	0.9986010
2153	1	0.9327884
2153	2	0.0672116
2154	1	0.7696447
2154	2	0.2303553
2155	1	0.0044184
2155	2	0.9955816
2156	1	0.8791344
2156	2	0.1208656
2157	1	0.7247708
2157	2	0.2752292
2158	1	0.1162869
2158	2	0.8837131
2159	1	0.0951943
2159	2	0.9048057
2160	1	0.0242717
2160	2	0.9757283
2161	1	0.0028995
2161	2	0.9971005
2162	1	0.9967035
2162	2	0.0032965
2163	1	0.3069074
2163	2	0.6930926
2164	1	0.1142309
2164	2	0.8857691
2165	1	0.0016962
2165	2	0.9983038
2166	1	0.3125352
2166	2	0.6874648
2167	1	0.0037304
2167	2	0.9962696
2168	1	0.9985162
2168	2	0.0014838
2169	1	0.8833651
2169	2	0.1166349
2170	1	0.1950431
2170	2	0.8049569
2171	1	0.4902608
2171	2	0.5097392
2172	1	0.2528628
2172	2	0.7471372
2173	1	0.9988506
2173	2	0.0011494
2174	1	0.2473516
2174	2	0.7526484
2175	1	0.9970748
2175	2	0.0029252
2176	1	0.4904220
2176	2	0.5095780
2177	1	0.9983734
2177	2	0.0016266
2178	1	0.9826843
2178	2	0.0173157
2179	1	0.1095111
2179	2	0.8904889
2180	1	0.9964174
2180	2	0.0035826
2181	1	0.9982521
2181	2	0.0017479
2182	1	0.8682632
2182	2	0.1317368
2183	1	0.7099795
2183	2	0.2900205
2184	1	0.8217145
2184	2	0.1782855
2185	1	0.1630826
2185	2	0.8369174
2186	1	0.9976258
2186	2	0.0023742
2187	1	0.4704603
2187	2	0.5295397
2188	1	0.1688351
2188	2	0.8311649
2189	1	0.1135460
2189	2	0.8864540
2190	1	0.9982654
2190	2	0.0017346
2191	1	0.4231995
2191	2	0.5768005
2192	1	0.0417234
2192	2	0.9582766
2193	1	0.0014770
2193	2	0.9985230
2194	1	0.3937365
2194	2	0.6062635
2195	1	0.7347173
2195	2	0.2652827
2196	1	0.9990446
2196	2	0.0009554
2197	1	0.9972350
2197	2	0.0027650
2198	1	0.9962012
2198	2	0.0037988
2199	1	0.9974503
2199	2	0.0025497
2200	1	0.6693511
2200	2	0.3306489
2201	1	0.0006172
2201	2	0.9993828
2202	1	0.2574151
2202	2	0.7425849
2203	1	0.9141922
2203	2	0.0858078
2204	1	0.0011852
2204	2	0.9988148
2205	1	0.1022311
2205	2	0.8977689
2206	1	0.0016296
2206	2	0.9983704
2207	1	0.3485956
2207	2	0.6514044
2208	1	0.3471464
2208	2	0.6528536
2209	1	0.1947241
2209	2	0.8052759
2210	1	0.9977936
2210	2	0.0022064
2211	1	0.5145979
2211	2	0.4854021
2212	1	0.2262607
2212	2	0.7737393
2213	1	0.6871701
2213	2	0.3128299
2214	1	0.9731414
2214	2	0.0268586
2215	1	0.0016108
2215	2	0.9983892
2216	1	0.4600580
2216	2	0.5399420
2217	1	0.0137289
2217	2	0.9862711
2218	1	0.3173113
2218	2	0.6826887
2219	1	0.9994701
2219	2	0.0005299
2220	1	0.3145587
2220	2	0.6854413
2221	1	0.5894141
2221	2	0.4105859
2222	1	0.5022471
2222	2	0.4977529
2223	1	0.4278817
2223	2	0.5721183
2224	1	0.0018471
2224	2	0.9981529
2225	1	0.0256444
2225	2	0.9743556
2226	1	0.4673791
2226	2	0.5326209
2227	1	0.9983248
2227	2	0.0016752
2228	1	0.2123862
2228	2	0.7876138
2229	1	0.1598188
2229	2	0.8401812
2230	1	0.8798912
2230	2	0.1201088
2231	1	0.0010877
2231	2	0.9989123
2232	1	0.9983908
2232	2	0.0016092
2233	1	0.4678134
2233	2	0.5321866
2234	1	0.9993522
2234	2	0.0006478
2235	1	0.0760146
2235	2	0.9239854
2236	1	0.2719584
2236	2	0.7280416
2237	1	0.2226268
2237	2	0.7773732
2238	1	0.1705999
2238	2	0.8294001
2239	1	0.9981672
2239	2	0.0018328
2240	1	0.6325998
2240	2	0.3674002
2241	1	0.0296735
2241	2	0.9703265
2242	1	0.9981244
2242	2	0.0018756
2243	1	0.9983112
2243	2	0.0016888
2244	1	0.1173408
2244	2	0.8826592
2245	1	0.7225991
2245	2	0.2774009
2246	1	0.9968596
2246	2	0.0031404

Cada um desses valores é uma proporção estimada de palavras de cada documento que foram geradas a partir de cada tópico. Por exemplo, o modelo estima que apenas cerca de 24,8% das palavras no documento 1 foram geradas a partir do tópico 1. Podemos ver que o documento 6 foi tirado quase inteiramente do tópico 2. Para verificar, podemos tidy() a DFM e checar quais as palavras comuns nesse documento.

tidy(AssociatedPress) %>%
  filter(document == 6) %>%
  arrange(desc(count))

## # A tibble: 287 × 3
##    document term           count
##       <int> <chr>          <dbl>
##  1        6 noriega           16
##  2        6 panama            12
##  3        6 jackson            6
##  4        6 powell             6
##  5        6 administration     5
##  6        6 economic           5
##  7        6 general            5
##  8        6 i                  5
##  9        6 panamanian         5
## 10        6 american           4
## # … with 277 more rows

9.2.1.2 Expressed Agenda Model

Projetado para medir como os autores dividem sua atenção sobre temas, o modelo apresenta outra maneira de explorar a mesma estrutura inaugurada pelo LDA⁸. Sua principal suposição é que cada autor divide sua atenção a um conjunto de tópicos. Assim, condicionado a tal distribuição de atenção dos atores, o tópico de cada documento é extraído.

O Expressed Agenda Model foi utilizado por Moreira (2020) para o caso brasileiro com o objetivo de identificar as ênfases temáticas proferidas pelos deputados e deputadas federais em seus discursos no Pequeno Expediente de 1999 a 2014. Com foco sobre a 54 legislatura, veremos a aplicação.

9.2.1.2.1 1. Definição do número de tópicos

Uma vez realizado o pré-processamento dos dados, o primeiro desafio imposto pelo modelo é a definição do número \(k\) de tópicos presente no corpus, ou seja, a quantidade de temas abordados em cada uma das legislaturas analisadas. Para a definição do número \(k\) de tópicos, duas estratégias foram utilizadas:

o uso de um modelo não paramétrico para clusterização de texto baseado no Dirichlet Process Prior Grimmer (2010);

O modelo não paramético resultou em 36 tópicos contidos no acervo. No entanto, dadas as ponderações já apresentadas, esse resultado não foi considerado de forma definitiva e a estipulação da quantidade \(k\) de tópicos contou com uma avaliação qualitativa do resultado de diferentes modelos para cada legislatura.

a estimação de diferentes modelos.

A avaliação qualitativa permite que o valor \(k\) seja definido pela coesão substantiva identificada pelo analista através da análise dos stems mais associados a cada tópico em diferentes modelos e da leitura de amostras aleatórias de documentos presentes nas categorias estimadas por cada modelo. Foram estimados e analisados os resultados de modelos que variaram de 5 a 80 tópicos.

Por um lado, comparados entre si, quanto menor o número de tópicos de um modelo, maior é a diversidade de discursos classificados em cada um, resultando em categorias muito genéricas. Por outro, quanto maior o número de tópicos do modelo, maior é a quantidade de tópicos tratando sobre o mesmo tema. Por essa razão, com o auxílio da evidência estatística do modelo não paramétrico, foi possível analisar de forma qualitativa os resultados dos 75 modelos estimados para a definição de um resultado de 39 tópicos para a legislatura 54.

O resultado do modelo com as 39 categorias podem ser encontrados na Figura 9.3 Moreira (2020).

Figure 9.3: Temas dos Discursos Proferidos na Legislatura 54

Na primeira coluna apresenta-se o rótulo dado a cada tópico após a leitura de uma amostra de ao menos dez discursos aleatoriamente selecionados de cada um deles. Na segunda coluna, é possível verificar até o quinto stem com maior informação mútua em cada tópico. Na terceira, é apresentado o percentual de documentos do corpus classificado em cada um dos tópicos.

9.2.1.2.2 Validação

Distintas formas de validação foram adotadas para averiguar se os resultados são substantivamente relevantes. Os tópicos foram validados por meio de quatro procedimentos:

dado que a matéria-prima para a análise dos tópicos são os stems das palavras contidas nos discursos, verificou-se quais os dez stems mais associados a cada um pelo do cálculo de sua informação mútua Grimmer and Stewart (2013);
foram lidos ao menos dez discursos aleatoriamente selecionados para rotulação de cada tópico;
sendo cada discurso pertencente a um tópico, é analisada sua pertinência temporal conforme a frequência dos tópicos ao longo da legislatura;
é qualitativamente analisada a dedicação de parlamentares selecionados a tópicos específicos, de modo que seja possível identificar se há coerência entre a classificação temática dos discuros e perfis parlamentares amplamente conhecidos e difundidos na sociedade e na ciência política brasileira.

Os resultados de dois dos quatro procedimentos de validação: a leitura atenta de uma amostra aleatória dos discursos presentes em cada tópico para rotulagem adequada e a análise de raízes com a maior informação mútua em cada um dos tópicos podem ser encontrados na Tabela apresentada.

Avançamos a seguir, portanto, no sentido de avaliar a pertinência temporal dos tópicos e a ênfase temática esperada de alguns parlamentares de perfil amplamente conhecido pela sociedade brasileira e a ciência política nacional.

Pertinência temporal de topicos selecionados: verifica-se se os discursos relacionados a cada tópico estão em acordo com debates desenvolvidos ao longo da legislatura e, em especial, se condizem com a ocorrência de eventos exógenos à instância de produção do discurso. Na figura a seguir, vemos a relevância do tema “ESPORTE” ao longo da legislatura analisada.

Ênfase temática de deputados federais selecionados: A principal contribuição do expressed agenda model, em comparação com as demais metodologias utlizadas na classificação de conteúdo de forma não supervisionada, é sua estrutura hierárquica que permite identificar a ênfase de temática de autores. Por tal razão, como última estratégia de validação dos resultados obtidos pelo modelo para as legislaturas analisadas, foram averiguadas as ênfases temáticas de deputados federais cujo perfil é amplamente difundido e conhecido na ciência política nacional.

Como se pode constatar, as falas proferidas pelo então deputado federal Romário possuem nítida relação com sua atuação parlamentar.

Em conjunto com os outros dois procedimentos de validação, a ênfase temática dos deputados federais identificada pelo Expressed Agenda Model e ilustrada com a apresentação dos resultados para o tema do esporte ao longo da legislatura e os pronunciamentos do deputado federal Romário indica que o modelo estimado foi satisfatório.

9.2.1.3 STM: Structural Topic Model:

A cientista política Margaret E. Roberts é a principal desenvolvedora do STM. Em conjunto com Brandon Stewart e Dustin Tingley ganhou o Political Methodology Society’s Statistical Software Award for 2018.

Com base na mesma estrutura do LDA, o STM inova com duas características. Em primeiro lugar, permite que, ao nível dos documentos, seus metadados⁹, entendidos enquanto covariáveis, sejam incorporados ao modelo. Logo, informações como autoria e data de publicação podem ser incluídas para contribuir com a estimação dos tópicos. Em segundo lugar, permite estimar a correlação entre os tópicos de modo a identificar, por exemplo, quando dois tópicos podem ocorrer simultaneamente num documento. Tal informação pode auxiliar o pesquisador na identificação de temas que transcendam os tópicos e, portanto, permita sua aglutinação.

Assim, com o objetivo de estimar a relação entre metadados e tópicos, no STM, estes são definidos como uma mistura sobre palavras em que cada palavra tem uma probabilidade de pertencer a um tópico. Logo, um documento é uma mistura sobre tópicos, o que significa que um único documento pode ser composto de vários tópicos, do mesmo modo como no caso do LDA.

O pacote stm disponível no R possui a seguinte estrutura heurística:

Vejamos uma aplicação do STM com base no seu tutorial e num acervo de postagens de blogs sobre a política americana¹⁰. Como covariável para estimação dos tópicos dos documentos, será utilizada blog ideology (rating) em conjunto com a ordem cronológica dos dias. Para realizar o exemplo, será necessário baixar os dados, disponível no link do tutorial do pacote.

# carregando pacotes ----
library(stm)        # Para modelagem de tópico estruturada
library(igraph)     # Para análise de rede e visualização
library(stmCorrViz) # Para visualizar a correlação hierarquica do STMs

Para preparação do corpus a função textProcessor realiza a retirada de stopwords e realiza o processo de steeming na base.

processed <- textProcessor(data$documents, metadata=data)

Utilizamos então a função prepDocuments para estruturar os dados para modelagem de tópico. O objeto não deve possuir valores faltantes. Palavras de baixa frequência podem ser removidas usando a opção lower.tresh.

out <- prepDocuments(processed$documents, processed$vocab, processed$meta)

Em seguida, salvamos o resultado do objeto, os documentos e vocabulário em variáveis:

docs <- out$documents
vocab <- out$vocab
meta <- out$meta

Para checar quantas palavras e documentos seriam removidos utilizando diferentes lower thresholds, a função plotRemoved() pode ser utilizada para visualização:

# associando texto com meta-dados ----
plotRemoved(processed$documents, lower.thresh=seq(1,200, by=100))

9.2.1.3.1 Estimação com a prevalência tópica

A prevalência tópica captura quanto cada tópico contribui para um documento. Como documentos diferentes vêm de fontes diferentes, é natural querer permitir que essa prevalência varie com os metadados que temos sobre as origens dos documentos. Para isso, executamos o modelo stm usando os dados de out com 20 tópicos. Aqui verificamos como a prevalência de tópico varia de acordo com os documentos e metadados. O pacote stm necessita que coloquemos o numéro máximo de expectation-maximization iterations \(=\) 75, e um seed para reproducibilidade.

poliblogPrevFit <- stm(out$documents, out$vocab, K=20, prevalence=~rating+s(day), 
                       max.em.its=75, data=out$meta, init.type="Spectral", 
                       seed=8458159)

9.2.1.3.2 Interpretando o STM: visualizando e inspecionando os resultados

Há muitas maneiras de investigar os resultados do modelo, como inspecionar as palavras associadas aos tópicos ou a relação entre metadados e tópicos. O pacote stm fornece diferentes opções/funções prontas para esse uso.

Exibindo palavras associadas a tópicos (labelTopics, plot.STM(, type = "labels"), sageLabels, plot.STM(, type = "perspectives")) ou documentos altamente associados a tópicos específicos (findThoughts, plotQuote).

# palavras mais associadas ----

labelTopics(poliblogPrevFit, c(3, 7, 20))

## Topic 3 Top Words:
##       Highest Prob: obama, democrat, poll, hillari, voter, state, vote 
##       FREX: superdeleg, deleg, primari, poll, pennsylvania, michigan, hampshir 
##       Lift: “expanded”, bredesen, callupd, caucus-go, cfi, enthus, errorth 
##       Score: hillari, poll, obama, mccain, voter, clinton, deleg 
## Topic 7 Top Words:
##       Highest Prob: obama, barack, campaign, senat, said, will, clinton 
##       FREX: blagojevich, barack, obama, illinoi, blago, lieberman, president-elect 
##       Lift: -watch, “fg, “transformational”, accommodationist, addedso, anti-lieberman, anti-nafta 
##       Score: obama, barack, blagojevich, hillari, campaign, clinton, lieberman 
## Topic 20 Top Words:
##       Highest Prob: will, american, america, countri, presid, nation, peopl 
##       FREX: america, countri, togeth, veteran, centuri, proud, must 
##       Lift: --young, amar, antietam, borderland, century…, crosscurr, doughboy 
##       Score: america, countri, presid, veteran, war, american, world

Por padrão, a função retorna o resultado de diferentes medidas, são elas:

Highest Prob: palavras com maior probabilidade;
FREX: pondera as palavras pela sua frequência geral e como elas são exclusivas para o tópico.
Lift: pondera as palavras dividindo por sua frequência em outros tópicos, dando, portanto, maior peso às palavras que aparecem com menos frequência em outros tópicos.
Score: divide o log da frequência da palavra no tópico pelo log de sua freqüência em outros tópicos.

Para examinar documentos altamente associados a tópicos, a função findThoughts pode ser usada. A função retorna os documentos altamente associados a cada tópico. A leitura desses documentos é útil para entender o conteúdo de um tópico e interpretar seu significado.

# documentos mais associados
thoughts3 <- findThoughts(poliblogPrevFit, texts = shortdoc,
                          n = 2, topics = 3)$docs[[1]]
thoughts20 <- findThoughts(poliblogPrevFit, texts = shortdoc,
                           n = 2, topics = 20)$docs[[1]]
par(mfrow = c(1, 2),mar = c(.5, .5, 1, .5))
plotQuote(thoughts3, width = 30, main = "Topic 3")
plotQuote(thoughts20, width = 30, main = "Topic 20")

Relacionamentos entre metadados e tópicos (estimateEffect). Essas relações podem desempenhar um papel fundamental na validação do modelo Ying, Montgomery, and Stewart (2022).

# Relacionamentos entre metadados e tópicos ----

out$meta$rating <- as.factor(out$meta$rating)
prep <- estimateEffect(1:20 ~ rating + s(day), poliblogPrevFit, meta = out$meta,
                       uncertainty = "Global")

summary(prep, topics=1)

## 
## Call:
## estimateEffect(formula = 1:20 ~ rating + s(day), stmobj = poliblogPrevFit, 
##     metadata = out$meta, uncertainty = "Global")
## 
## 
## Topic 1:
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.004059   0.009114   0.445 0.656070    
## ratingLiberal 0.020746   0.002346   8.842  < 2e-16 ***
## s(day)1       0.041205   0.017320   2.379 0.017376 *  
## s(day)2       0.019688   0.011140   1.767 0.077188 .  
## s(day)3       0.006901   0.012429   0.555 0.578753    
## s(day)4       0.032070   0.011077   2.895 0.003795 ** 
## s(day)5       0.034652   0.011664   2.971 0.002976 ** 
## s(day)6       0.013289   0.011664   1.139 0.254617    
## s(day)7       0.043176   0.011248   3.838 0.000124 ***
## s(day)8       0.059298   0.015728   3.770 0.000164 ***
## s(day)9       0.024080   0.014236   1.692 0.090759 .  
## s(day)10      0.015492   0.014222   1.089 0.276045    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Para ver como a prevalência de tópicos difere entre os valores de uma covariável categórica:

plot(prep, covariate="rating", topics=c(3, 7, 20), model=poliblogPrevFit, 
     method="difference", cov.value1="Liberal", cov.value2="Conservative",
     xlab="More Conservative ... More Liberal", 
     main="Effect of Liberal vs. Conservative",
     xlim=c(-.15,.15), labeltype ="custom", custom.labels=c('Obama', 'Sarah Palin', 
                                                          'Bush Presidency'))

Calculando correlações dos tópicos (topicCorr). Podemos também visualizar a correlação entre tópicos, correlações positivas entre tópicos indicam que ambos os tópicos provavelmente serão discutidos em um documento. A rede de relações demonstra o quão próximos os tópicos são entre sí, ou seja o quão provavél serão discutidos em um documento.

mod.out.corr <- topicCorr(poliblogPrevFit)
plot(mod.out.corr)

9.2.1.3.3 STM - Comunicando os resultados

Corpus: A visualização no nível do corpus pode ser feita em relação à proporção esperada de cada tópico no corpus.

# proporção esperada de cada tópico no corpus ----
plot(poliblogPrevFit, type = "summary", xlim = c(0, .3))

Vemos no exemplo que o tópico Sarah Palin / Vice-President (7) tem pequena presença no corpus. O tópico mais comum é um tópico geral cheio de palavras que os blogueiros geralmente usam e, portanto, não é muito interpretável. As palavras listadas na figura são as três principais palavras associadas ao tópico.

Relação Metadado/tópico: A capacidade de estimar relacionamentos de metadados/tópicos é uma vantagem central do modelo STM. Quando a covariável de interesse é binária, há interesse em um contraste particular. No exemplo a seguir, a opção method = “difference” irá traçar a mudança na proporção do tópico mudando de um valor específico para outro.

# Relação Metadata/topico: ----
plot(prep, covariate = "rating", topics = c(3, 7, 20),
     model = poliblogPrevFit, method = "difference",
     cov.value1 = "Liberal", cov.value2 = "Conservative",
     xlab = "More Conservative ... More Liberal",
     main = "Effect of Liberal vs. Conservative",
     xlim = c(-.1, .1), labeltype = "custom",
     custom.labels = c('Obama', 'Sarah Palin','Bush Presidency'))

Vemos que o Tópico 1 é fortemente usado pelos conservadores em comparação aos liberais, enquanto o Tópico 7 está próximo do meio, mas ainda conservador. O tópico 10, relacionado a Bush, foi amplamente associado a escritores liberais, o que está de acordo com a tendência observada de distanciamento conservador de Bush após sua presidência.

Para tratar variáveis contínuas, pode-se assumir um ajuste linear ou usar splines. No exemplo anterior, permitimos que a variável day tivesse um relacionamento não linear no estágio de estimativa de tópico. Podemos então traçar o seu efeito nos tópicos. Abaixo, plotamos a relação entre o tempo e o tópico vicepresidencial, tópico 7. O tópico tem um pico quando Sarah Palin se tornou companheira de chapa de John McCain no final de agosto de 2008.

# Relação Metadata/topico: ----
plot(prep, "day", method = "continuous", topics = 7,
     model = z, printlegend = FALSE, xaxt = "n", xlab = "Time (2008)")
monthseq <- seq(from = as.Date("2008-01-01"),
                to = as.Date("2008-12-01"), by = "month")
monthnames <- months(monthseq)
axis(1,at = as.numeric(monthseq) - min(as.numeric(monthseq)), labels = monthnames)

Plotando a interação entre covariáveis: Neste exemplo, reestimamos o STM para permitir uma interação entre o day (inserido linearmente) e rating. Exibimos os resultados na figura para o tópico 20 (administração Bush). Observamos que os conservadores nunca escreveram muito sobre esse assunto, enquanto os liberais discutiram muito esse tópico, mas com o tempo o assunto diminuiu em importância.

# Interacao covariaveis ----

prep <- estimateEffect(c(20) ~ rating * day, poliblogInteraction,
                       metadata = out$meta, uncertainty = "None")
plot(prep, covariate = "day", model = poliblogInteraction,
     method = "continuous", xlab = "Days", moderator = "rating",
     moderator.value = "Liberal", linecol = "blue", ylim = c(0, .12),
     printlegend = F)

plot(prep, covariate = "day", model = poliblogInteraction,
     method = "continuous", xlab = "Days", moderator = "rating",
     moderator.value = "Conservative", linecol = "red", add = T,
     printlegend = F)
legend(0, .08, c("Liberal", "Conservative"), lwd = 2, col = c("blue", "red"))

Tópico: Também podemos traçar a influência de uma covariável no conteúdo de um tópico. Uma variável de conteúdo temático permite que o vocabulário usado para falar sobre um determinado tópico varie. Abaixo, as diferenças de vocabulário por rating são plotadas para o tópico 11. O tópico 11 está relacionado a Guantánamo.

Suas principais palavras FREX foram “tortur, detaine, court, justic, interrog, prosecut, legal”. A figura nos mostra como liberais e conservadores falam sobre esse tópico de maneira diferente. Em particular, os liberais enfatizaram a “tortur”, enquanto os conservadores enfatizam a linguagem típica dos tribunais, como “illegal” e “law”.

# Topico/covariavel: ----
plot(poliblogContent, type = "perspectives", topics = 11)

Essa função também pode ser usada para plotar o contraste de palavras em dois tópicos.

# Topico/contrastes: ----
plot(poliblogPrevFit, type = "perspectives", topics = c(12, 20))

9.2.2 Atividade Prática

Com a base de dados dos pronunciamentos registrados no dia da aprovação do processo de impeachment da Presidenta Dilma, utilize o LDA e classifique o conteúdo proferido em 2 categorias. Diante desse \(k\) de categorias, é possível tirar alguma conclusão do acervo?

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