This post outlines a fairly simple workflow from annotated corpus to topic model, with a focus on the exploratory utility of topic models. We first consider some text structures relevant to topic modeling in R, and then demonstrate some approaches to visualizing model results, including variation in topic prevalence over time for a diachronic corpus. Lastly, we consider methods for visualizing relatedness among topics.
For demonstration purposes, we utilize a set of corpora made avaiable via the
library(tidyverse) library(topicmodels) library(tidytext) library(spacyr) library(corpusdatr)#devtools::install_github("jaytimm/corpusdatr") library(corpuslingr)#devtools::install_github("jaytimm/corpuslingr")
Very quickly, topic modeling is an unsupervised text classification methodology in which
- documents are modeled as composites of topics, and
- topics are modeled as composites of features/words.
The approach to topic modeling employed here is latent Dirichlet allocation (LDA); to fit our models, we use the
LDA function from the
topicmodels package (Hornik and Grün 2011).
In our first example, we investigate topics in the annotated Slate Magazine corpus (ca 1996-2000, 1K texts, 1m words), available as
cdr_slate_ann from the
corpusdatr package. The corpus has been annotated using the
spacyr package, and is functionally a synchronic (or static) corpus by virtue of not containing publication date information.
The first step in building the model is to summarize our corpus; here, this means representing each document in the corpus in terms of its constitutent features and feature frequencies, ie, as a bag-of-words (BOW). To accomplish this task, we use the
clr_get_freq function from the
corpuslingr package. We set the lemma as our feature unit, and limit feature composition to nouns and entities.
dtm <- corpusdatr::cdr_slate_ann %>% spacyr::entity_consolidate() %>% filter(tag %in% c("NN", "NNS") | pos =='ENTITY')%>% corpuslingr::clr_get_freq(agg_var=c('doc_id','lemma'), toupper=FALSE)%>% arrange(doc_id)
Example portion of the corpus-as-BOW:
## doc_id lemma txtf docf ## 1 1 noise 14 25 ## 2 1 lip 9 24 ## 3 1 talk 7 150 ## 4 1 walk 6 24 ## 5 1 one 5 874 ## 6 1 service 5 177
Like most topic modeling functions,
topicmodels::LDA requires a document-term matrix (DTM) as input. A DTM is a sparse matrix and, as a data structure, is awkward to work with. Conveniently, the
cast_sparse function from the
tidytext package allows us to get from corpus-as-BOW to formal DTM quite easily.
Per the output of
clr_get_freq, we filter out features with extreme document frequencies (ie, features with limited utility in classification), and then build the DTM with
static_DTM <- dtm%>% filter(docf < 500 & docf > 5)%>% tidytext::cast_sparse(row=doc_id,column=lemma,value=txtf)
Which has the following structure:
## Formal class 'dgCMatrix' [package "Matrix"] with 6 slots ## [email protected] i : int [1:161352] 0 18 20 104 191 197 229 254 280 291 ... ## [email protected] p : int [1:5435] 0 25 49 199 223 400 540 596 1001 1067 ... ## [email protected] Dim : int [1:2] 1000 5434 ## [email protected] Dimnames:List of 2 ## .. ..$ : chr [1:1000] "1" "10" "100" "1000" ... ## .. ..$ : chr [1:5434] "noise" "lip" "talk" "walk" ... ## [email protected] x : num [1:161352] 14 1 1 1 1 1 1 1 1 2 ... ## [email protected] factors : list()
Lastly, we fit the model, specifying an eight topic solution:
static_topic <- topicmodels::LDA(static_DTM, k = 8, control=list(seed=12)) #11
We extract the
terms object from the
LDA output using the
topicmodels::posterior function; output includes the posterior probabilities of the terms for each topic. Focusing on the six highest probability terms per topic, the plot below summarizes model results for the Slate Magazine corpus (ca 1996-2000).
library(ggthemes) topicmodels::posterior(static_topic)$terms %>% data.frame() %>% mutate(topics = row.names(.))%>% gather(key="term",value="beta", noise:wool) %>% group_by(topics) %>% top_n(6, beta) %>% ungroup() %>% arrange(topics, beta)%>% mutate(order = row_number(), term=factor(paste(order,term,sep="_"), levels = paste(order, term, sep = "_")), topics = as.character(topics))%>% ggplot(aes(x=term, y=beta, fill=topics)) + geom_col(show.legend = FALSE) + facet_wrap(~topics, scales = "free_y", ncol = 2) + scale_x_discrete(labels = function(x) gsub("^.*_", "", x))+ theme_fivethirtyeight()+ scale_fill_stata() + coord_flip()+ theme(plot.title = element_text(size=14)) + labs(title="Topic composition by feature") #
So, some curious times, the close of the 20th century: A lame-duck president in some hot water, presidential primaries, a war abroad. The model seems to paint a fairly clear picture of the socio-political happenings of the time period, and provides a nice macro-vantage from which to view/explore corpus content.
Next we explore topics in a diachronic corpus, and demonstrate a straightforward approach to visualizing variation in topic prevalence over time. Here we use the
cdr_gnews_historical corpus from the
corpusdatr package for demonstration purposes.
Corpus and some descriptives
The corpus is comprised of web-based news articles published during a three-week time period (11-27/17 to 12/20/17). Articles were retrieved using my
quicknews package, which leverages Goggle News’ RSS feed to direct search, and annotated using the
For the sake of avoiding copyright issues, each constituent article in the corpus has already been reduced to a BOW. The corpus is comprised of ~1,500 texts, ~1.3 million words, and ~200 unique media sources.
Example corpus metadata:
head(cdr_gnews_meta)[1:4] ## doc_id pubdates source ## 1 1 2017-11-27 New York Times ## 2 2 2017-11-27 New York Times ## 3 3 2017-11-27 Washington Post ## 4 4 2017-11-27 CNN ## 5 5 2017-11-27 CNN ## 6 6 2017-11-27 Washington Post ## titles ## 1 2 Bosses Show Up to Lead the Consumer Financial Protection Bureau ## 2 Meghan Markle Is Going to Make History ## 3 Trump could personally benefit from last-minute change to Senate tax bill ## 4 Melania Trump unveils White House holiday decor ## 5 Trump's latest conspiracy? The 'Access Hollywood' tape was a fake! ## 6 Trump attacks media in his first post-Thanksgiving tweet
Some basic corpus descriptives:
cdr_gnews_meta%>% group_by(pubdates) %>% summarize_at(vars(docN),funs(sum))%>% ggplot(aes(x=pubdates, group = 1)) + geom_line(aes(y=docN), size=1.25, color = 'steelblue') + labs(title="Daily corpus size", subtitle = "11-27-17 to 12-20-17")
The corpus is comprised predominantly of articles from the Washington Post, CNN, and the New York Times. It is unclear if these sources generate the most content, or if this is a bias of the news aggregator, or if these sites care less about folks scraping content from their sites.
cdr_gnews_meta %>% group_by(source) %>% summarize_at(vars(docN),funs(sum))%>% top_n(10,docN)%>% ggplot(aes(x=reorder(source, docN), y=docN)) + geom_col(width=.65, fill='steelblue') + coord_flip()+ labs(title="Top ten news sources by text frequency", subtitle = "11-27-17 to 12-20-17")
We follow the same procedure to fitting the topic model as we did previously:
hist_topic <- corpusdatr::cdr_gnews_historical %>% filter(tag %in% c("NN", "NNS") | pos =='ENTITY')%>% group_by(doc_id,lemma) %>% summarize_at(vars(freq),funs(sum))%>% tidytext::cast_sparse(row=doc_id,column=lemma,value=freq)%>% topicmodels::LDA(., k = 12, control = list(verbose = 0, seed=999))
Extract the topic summary of the model:
topic_summary <- data.frame(topicmodels::terms(hist_topic,7)) %>% gather(key='topic',value='val',Topic.1:Topic.12) %>% group_by(topic)%>% summarize (dims = paste(val,collapse=', '))%>% mutate(topic = as.numeric(gsub('Topic.','',topic)))%>% arrange(topic)
Topics over the three week period:
## # A tibble: 12 x 1 ## dims ## <chr> ## 1 tax, bill, Senate, Republicans, House, vote, rate ## 2 Moore, Alabama, woman, voter, election, Republican, Trump ## 3 woman, allegation, harassment, story, time, statement, people ## 4 film, time, first, one, people, movie, way ## 5 North Korea, missile, snow, U.S., weapon, North Korean, report ## 6 company, deal, Fox, market, price, time, Disney ## 7 EU, deal, government, trade, Yankees, time, Britain ## 8 fire, wildfire, home, man, photo, Ventura, people ## 9 Iran, Yemen, CNN, people, coalition, missile, time ## 10 game, team, time, coach, player, first, NFL ## 11 Trump, president, Russia, Trump_'s, official, Mueller, Flynn ## 12 police, Jerusalem, Israel, officer, death, city, people
So a busy three weeks. The special Senate election in Alabama (and surrounding controversey), wildfires in California, North Korea, the Mueller investigation, tax reform, the #MeToo movement.
Topic prevalence historically
In order to quantify the prevalence of these topics over time, we shift focus from topic composition in terms of words/features to document composition in terms of topics. So, we first extract the posterior probabilities of the topics for each document; then we join corpus metadata and topic summary details.
hist_beta <- topicmodels::posterior(hist_topic)$topics %>% data.frame() %>% mutate(doc_id = row.names(.))%>% arrange(as.numeric(doc_id))%>% left_join(cdr_gnews_meta) %>% gather(key="topic",value="val",X1:X12) %>% mutate(topic = as.numeric(gsub('X','',topic)))%>% left_join(topic_summary)
Based on this set of model results, each document in our corpus can be represented as a composite of the sixteen topics summarized above; topic composites for an example set of texts are illustrated in the figure below. Per the figure, text 183 is comprised (in varying degrees) of topics 2, 3, 4, and 9.
hist_beta %>% filter(doc_id %in% c('183', '631','896')) %>% ggplot(aes(x=reorder(paste(topic,dims, sep=" - "), -topic), y=val, fill = dims)) + geom_col(width=.85) + coord_flip()+ scale_fill_stata() + theme(axis.text.x = element_text(angle = 90))+ xlab ("topic") + ylab("beta") + facet_wrap(~doc_id)+ labs(title="Text by topic") + theme(legend.position="none")
Topic prevalence over time, then, is simply the aggregate of these topic probabilities for each document by publication date.
agg_hist_beta <- hist_beta %>% group_by(pubdates,topic,dims) %>% summarize_at(vars(val),funs(sum))%>% ungroup()
Finally, we plot the results. The size of plot points represents aggregate posterior probabilities, which can be interpreted as the likelihood that some article a written on day d was about some topic z.
The top six words associated with each topic are displayed as well. For a relatively small corpus (comprised of a wide range of content), the plot provides a nice overview of variation in topic prevalence over time.
p <- ggplot(agg_hist_beta) + geom_point(aes(x = pubdates, y = reorder(topic,-topic), size = val, color=dims)) + theme_fivethirtyeight() + scale_color_stata() p + geom_text(data = agg_hist_beta[agg_hist_beta$pubdates == "2017-11-27",], aes(x = pubdates, y = reorder(topic,-topic), label = dims), vjust=-1, hjust=0) + labs(title="Topic prevalence over time", subtitle="11-27-2017 to 12-20-2017")+ theme(legend.position="none", plot.title = element_text(size=14))
Lastly, we consider the relationship among topics in the
cdr_gnews_historical corpus via cluster analysis. The first step in this process is to create a correlation matrix of the beta values for constituent topic features.
cor_mat <- data.frame(posterior(hist_topic)$topics)%>% `colnames<-`(paste(topic_summary$topic,topic_summary$dims, sep=" - ")) %>% cor(.)
Next, we compute the distances (ie, dissimilarities) between topic-pairs, and perform hierarchical clustering analysis on the resulting matrix. We use the
ggdendro package to plot results. Per plot below, some intuitive relationships, some less so.
library(ggdendro) hclust(dist(cor_mat)) %>% ggdendrogram(., rotate=TRUE) + theme_fivethirtyeight()
A brief outline for quick topic modeling, with some different applications for synchronic and diachronic corpora. For a smarter discussion of underlying assumptions and maths, see Hornik and Grün (2011).
Hornik, Kurt, and Bettina Grün. 2011. “Topicmodels: An R Package for Fitting Topic Models.” Journal of Statistical Software 40 (13). American Statistical Association: 1–30.