rm(list=ls(all=TRUE))
getwd()
setwd("C:/Users/luigi/Dropbox/TOPIC MODEL/FOR NEXT YEAR 2020/Newsmap")
getwd()
library(quanteda)
library(readtext)
library(ggplot2)
library(ldatuning)
library(topicmodels)
library(lubridate)
library(topicdoc)
library(cowplot)
#########################################
########## Topic-model (LDA)
#########################################
# This data frame contains 1,959 Guardian news articles published in 2016
myText <- read.csv("guardian.csv", stringsAsFactors=FALSE)
str(myText)
# creating a new variable "text2". Later it will be useful
myText$text2 <- myText$text
news_corp <- corpus(myText)
head(summary(news_corp))
tok2 <- tokens(news_corp, remove_punct = TRUE, remove_numbers=TRUE, remove_symbols = TRUE, split_hyphens = TRUE, remove_separators = TRUE)
tok2 <- tokens_remove(tok2, stopwords("en"))
tok2 <- tokens_wordstem (tok2)
news_dfm <- dfm(tok2)
# let's remove the reference to time
news_dfm <- dfm_remove(news_dfm, c('*-time', '*-timeUpdated', 'GMT', 'BST'))
# We keep only the top 5% of the most frequent features min_termfreq = 0.95) that appear less than 10% in all
# the documents (max_docfreq = 0.1) using dfm_trim() to focus on common but distinguishing features.
news_dfm <- dfm_trim(news_dfm, min_termfreq = 0.95, termfreq_type = "quantile",
max_docfreq = 0.1, docfreq_type = "prop")
news_dfm[1:2, 1:5]
# 20 top words
topfeatures(news_dfm, 20)
# Let's also keep only documents with number of tokens >0 [given that after the above reduction,
# some articles could actually be empty - 8 here]
news_dfm[ntoken(news_dfm) == 0,]
news_dfm <- news_dfm[ntoken(news_dfm) > 0,]
# as docvars there is the text as well (text2) - it will be helpful later on!
str(news_dfm@docvars)
# quanteda does not implement own topic models, but you can easily access to LDA() from the topicmodel package
# through convert(). k = 5 specifies the number of topics to be discovered.
dtm <- convert(news_dfm, to = "topicmodels")
# let's identify k=5
# being a probabilist model, always a good idea to declare the seed!
set.seed(123)
system.time(lda <- LDA(dtm, method= "Gibbs", k = 5)) # around 20 seconds on my laptop
# alternative way to define the seed:
# system.time(lda <- LDA(dtm, method= "Gibbs", k = 5, control = list(seed = 123)))
# We can use get_terms to the top n terms from the topic model (i.e., terms with the highest beta associated with each topic)
# and get_topics to predict the top k topic for each document. This will help us interpret the results of the model.
# Which the most likely terms for each topic?
terms <- get_terms(lda, 10)
# 10 terms for topic 1
terms[,1]
# 10 terms for topic 2, and so on
terms[,2]
terms[,3]
terms[,4]
terms[,5]
# or we can extract directly the most important terms from the model using terms().
terms(lda, 15)
# which meaning for each topic according to you??
# which is the most likely topic for each document?
topics <- get_topics(lda, 1)
head(topics, 10)
# or you can obtain the most likely topics using topics() and save them as a document-level variable.
head(topics(lda))
docvars(news_dfm, 'pred_topic') <- topics(lda)
str(news_dfm)
# Let’s take a closer look at some of these topics. To help us interpret the output, we can look
# at the words associated with each topic and take a random sample of documents highly associated
# with each topic.
# Topic 1
paste(terms[,1], collapse=", ")
set.seed(123)
sample(news_dfm@docvars$text2[topics==1], 2)
# Topic 2
paste(terms[,2], collapse=", ")
set.seed(123)
sample(news_dfm@docvars$text2[topics==2], 2)
# Topic 3
paste(terms[,3], collapse=", ")
set.seed(123)
sample(news_dfm@docvars$text2[topics==3], 3)
# Looking at the evolution of certain topics over time can be interesting
# Let’s look for example at Topic 2, which appears to be related somehow to Isis
str(news_dfm@docvars$date)
news_dfm@docvars$month <- substr(news_dfm@docvars$date, 1, 7) # extract year and month (first 7 characters)
table(news_dfm@docvars$month )
# frequency table with articles about Isis as their MAIN topic, per month in 2016
tab <- table(news_dfm@docvars$month [news_dfm@docvars$pred_topic==2])
plot(tab)
# But we can actually do better than this. LDA is a probabilistic model,
# which means that for each document, it actually computes a distribution over topics.
# In other words, each document is considered to be about a mixture of topics as we have already discussed!
# This information is included in the matrix gamma in the LDA object (theta in the notation we used for the slides)
# For example, news 1 is 26% about topic 4, while news 3 is 39% about topic 4
round(lda@gamma[1,], 2)
round(lda@gamma[3,], 2)
# So we can actually take the information in the matrix and aggregate it to compute the average probability that a news each month
# is about a particular topic. Let’s now choose once again Topic 2
# add probability to dfm
news_dfm@docvars$topic5 <- lda@gamma[,2]
str(news_dfm)
# now aggregate at the month level
agg <- aggregate(news_dfm@docvars$topic5, by=list(month=news_dfm@docvars$month), FUN=mean)
str(agg)
agg$date <- as.Date(paste(agg$month,"-01",sep=""))
str(agg)
# and plot it
plot(agg$date , agg$x, type="l", xlab="Month", ylab="Avg. prob. of article about topic 2 (ISIS)",
main="Estimated proportion of articles discussing about ISIS")
##################################
# identifying the optimal number of topics between for example: 4 and 15
##################################
##################################
### FIRST ALTERNATIVE - this alternative is the same one that we will employ also in the
### next lecture, when we discuss about STM (structural topic models)
##################################
# let's focus on topic coherence and exclusivity of our models with k=5
topic_diagnostics(lda, dtm)
topic_coherence(lda, dtm)
topic_exclusivity(lda)
mean(topic_coherence(lda, dtm))
mean(topic_exclusivity(lda))
# now let's make k changes between 4 and 15
top <- c(4:15)
top
# let's create an empty data frame that we will fill later on
results <- data.frame(first=vector(), second=vector(), third=vector())
results
# with add iter=100 where iter is the number of Gibbs iterations to make LDA faster; the default value is 2000 [so this is not an advisable strategy
# for a serious project!]
system.time(
for (i in top)
{
set.seed(123)
lda <- LDA(dtm, method= "Gibbs", k = (i), control=list(verbose=50L, iter=100))
topic <- (i)
coherence <- mean(topic_coherence(lda, dtm))
exclusivity <- mean(topic_exclusivity(lda))
results <- rbind(results , cbind(topic, coherence, exclusivity ))
}
)
results
str(results)
# You should focus on those models that lie on the semantic coherence-exclusivity ‘frontier’, that
# is, where no model strictly dominates another in terms of semantic coherence and
# exclusivity (i.e., models with average scores towards the upper right side of the plot).
# The figure below identifies model k=11 or k=15 as models that satisfy the
# ‘frontier’ criterion, i.e., models with desirable properties in both dimensions.
# These two values could be a good starting point. But remember: always validate your results by understanding the topics!
plot(results$coherence, results$exclusivity, main="Scatterplot Example",
xlab="Semantic Coherence", ylab="Exclusivity ", pch=19)
text(results$coherence, results$exclusivity, labels=results$topic, cex= 1, pos=4)
#####################################
### SECOND ALTERNATIVE - OPTIONAL
#####################################
# An alternative metric for evaluating topic models is the ‘held out likelihood’, also referred to as ‘perplexity’.
# The perplexity metric is calculated by splitting your original corpus into two parts – a training set and a test set.
# The basic idea is to hold out some fraction of the words in a set of documents, train the model and use the document-level latent variables to evaluate the probability
# of the heldout portion (i.e., previously unseen document).
# In words: for each document, show a fraction of words, use the learned topic matrix to predict the distribution Pr[z = i|doc],
# the probability of word z to belong to topic i in that document. The lower the perplexity, the better the model.
# Similar to the idea of cross-validation (we'll discuss a lot about it later on), when some of the data is removed from estimation and then later used for
# validation, the held-out likelihood helps the user assess the model’s prediction performance.
# In other words, perplexity metric assesses a topic model's ability to predict a test set after having been trained on a training set.
# Note however that Perplexity is not strongly correlated to human judgment
# Chang et al. have shown that, surprisingly, predictive likelihood (or equivalently, perplexity) and human judgment are often not correlated, and even sometimes slightly anti-correlated.
# Chang, Jonathan, Jordan Boyd-Graber, Sean Gerrish, Chong Wang and David M. Blei. 2009. Reading Tea Leaves: How Humans Interpret Topic Models. NIPS.
# Therefore, better focusing on coherence and exclusivity as above!
# let's extract 80% of the observations in our dtm and let's consider them as our training-set
# [why 80% and not 75% or 70%? actually it would be more elegant to use a fully cross-validation approach...what is that? More on this later on!]
nrow(dtm )
set.seed(123)
index <- sample(1:nrow(dtm), nrow(dtm )*0.8 )
length(index)
train_data <- dtm[index,]
test_data <- dtm[-index,]
nrow(train_data)
nrow(test_data)
# now let's make k changes between 4 and 15
topics <- seq(4, 15, by=1)
topics
# let's create an empty data frame that we will fill later on
# let's also save the log likelihood of the topic model for each single value of k (the higher is the log-likelihood, the better is)
perplexity_df <- data.frame(topic=vector(), test_perplexity=vector())
str(perplexity_df)
set.seed(12345)
system.time(
for (i in topics){
fitted <- LDA(train_data, k = i, method = "Gibbs", control=list(verbose=50L, iter=100))
perplexity_df[i,1] <-(i)
perplexity_df[i,2] <- perplexity(fitted, newdata = test_data) # A low perplexity indicates the probability distribution is good at predicting the sample
})
perplexity_df
perplexity_df <- na.omit(perplexity_df)
perplexity_df
ggplot(perplexity_df, aes(x= as.numeric(row.names(perplexity_df)), y=test_perplexity)) + geom_line(color="red", size=2) + ggtitle("Perplexity of held-out data") +
labs(x = "Candidate number of topics", y = "Perplexity")
##################################
### THIRD ALTERNATIVE - optional
##################################
system.time(tune <- FindTopicsNumber(dtm, metrics = c("Griffiths2004", "CaoJuan2009", "Arun2010", "Deveaud2014"),
topics = seq(4, 15, by=1),
control = list(seed = 123, iter=100)))
# Minimize:
# Density (Cao Juan et al. 2009)
# Within-Topic Divergence (Arun et al. 2010)
# Maximize:
# Across-Topic Divergence (Deveaud et al. 2014)
# log-likelihood (Griffiths and Steyvers 2004) (Collapsed Gibb Sampler only)
tune
FindTopicsNumber_plot(tune) # here 9 probably the best solution
##################################
### let's replicate our analysis but using the 2013 Guardian's articles
##################################
myText2 <- read.csv("guardian2013.csv", stringsAsFactors=FALSE)
str(myText2)