rm(list=ls(all=TRUE))
getwd()
# with setwd() you change the current working directory of the R process
# setwd("C:/Users/mw/Dropbox (VOICES)/TOPIC MODEL/FOR NEXT YEAR 2020/Newsmap")
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))

# After removal of function words and punctuations in dfm(), 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(news_corp, remove =(stopwords("english")), tolower = TRUE, stem = TRUE,
                remove_punct = TRUE, remove_numbers=TRUE)
news_dfm <- dfm_remove(news_dfm, c('*-time', '*-timeUpdated', 'GMT', 'BST')) 
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)

# keeping 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)

# 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))

# 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
str(news_dfm)

paste(terms[,1], collapse=", ")
sample(news_dfm@docvars$text2[topics==1], 2)

# Topic 3
paste(terms[,3], collapse=", ")
sample(news_dfm@docvars$text2[topics==3], 2)

# Topic 3
paste(terms[,5], collapse=", ")
sample(news_dfm@docvars$text2[topics==5], 3)

# Looking at the evolution of certain topics over time can be interesting
# Let’s look for example at Topic 5, which appears to be related 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==5])
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 22% about topic 1, while news 3 is 38% about topic 1
round(lda@gamma[1,], 2)
round(lda@gamma[3,], 2)
round(lda@gamma[7,], 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 5

# add probability to dfm
news_dfm@docvars$topic5 <- lda@gamma[,5]
str(news_dfm)
# now aggregate at the month level
agg <- aggregate(news_dfm@docvars$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 5",
     main="Estimated proportion of articles discussing about ISIS")

##################################
# identifying the optimal number of topics between for example: 4 and 9
##################################

##################################
### 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 9
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)

# k=9 seems to be the best choice
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
#####################################

# Another traditional metric for evaluating topic models is the ‘held out likelihood’, also referred to as ‘perplexity’.
# The perplexity metric is a predictive one and it is calculated by splitting your original corpus into two parts – a training set and a test set. 
# The idea is to train a topic model using the training set and then test the model on a test set which contains previously unseen documents 
# (i.e., held out documents). In other words, perplexity metric assesses a topic model's ability to predict a test set after having been trained on a training set. 

# 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 9
topics <- seq(4, 9, 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(), log=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[i,3]  <- logLik(fitted) 
})

perplexity_df
perplexity_df <- na.omit(perplexity_df)
perplexity_df

perp <- 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")
log <- ggplot(perplexity_df, aes(x= as.numeric(row.names(perplexity_df)), y=log)) +  geom_line(size=2) + ggtitle("Log-Likelihood") +
labs(x = "Candidate number of topics", y = "Log-Likelihood")

plot_grid(perp , log)

##################################
### THIRD ALTERNATIVE - optional
##################################

system.time(tune <- FindTopicsNumber(dtm, metrics = c("Griffiths2004", "CaoJuan2009", "Arun2010", "Deveaud2014"),
                             topics = seq(4, 9, by=1), 
                             control = list(seed = 123, iter=100)))

# Minimization: CaoJuan2009 and Arun2010 metrics
# Maximization: Deveaud2014 and Griffiths2004 metrics
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)