LDA_TopicModeling.R

# read in the libraries we're going to use
library(tidyverse) # general utility & workflow functions
library(tidytext) # tidy implimentation of NLP methods
library(topicmodels) # for LDA topic modelling 
library(tm) # general text mining functions, making document term matrixes
library(SnowballC) # for stemming
# read in our data
tweets <- readLines("/Users/frances/Desktop/MSc Project/Project Docs/CleanTweetsOnly_new.csv",encoding="UTF-8")
# function to get & plot the most informative terms by a specificed number
# of topics, using LDA
top_terms_by_topic_LDA <- function(input_text, # should be a columm from a dataframe
                                   plot = T, # return a plot? TRUE by defult
                                   number_of_topics = 4) # number of topics (4 by default)
{    
  # create a corpus (type of object expected by tm) and document term matrix
  Corpus <- Corpus(VectorSource(input_text)) # make a corpus object
  DTM <- DocumentTermMatrix(Corpus) # get the count of words/document
  
  # remove any empty rows in our document term matrix (if there are any 
  # we'll get an error when we try to run our LDA)
  unique_indexes <- unique(DTM$i) # get the index of each unique value
  DTM <- DTM[unique_indexes,] # get a subset of only those indexes
  
  # preform LDA & get the words/topic in a tidy text format
  lda <- LDA(DTM, k = number_of_topics, control = list(seed = 1234))
  topics <- tidy(lda, matrix = "beta")
  
  # get the top ten terms for each topic
  top_terms <- topics  %>% # take the topics data frame and..
    group_by(topic) %>% # treat each topic as a different group
    top_n(10, beta) %>% # get the top 10 most informative words
    ungroup() %>% # ungroup
    arrange(topic, -beta) # arrange words in descending informativeness
  
  # if the user asks for a plot (TRUE by default)
  if(plot == T){
    # plot the top ten terms for each topic in order
    top_terms %>% # take the top terms
      mutate(term = reorder(term, beta)) %>% # sort terms by beta value 
      ggplot(aes(term, beta, fill = factor(topic))) + # plot beta by theme
      geom_col(show.legend = FALSE) + # as a bar plot
      facet_wrap(~ topic, scales = "free") + # which each topic in a seperate plot
      labs(x = NULL, y = "Beta") + # no x label, change y label 
      coord_flip() # turn bars sideways
  }else{ 
    # if the user does not request a plot
    # return a list of sorted terms instead
    return(top_terms)
  }
}
# plot top ten terms in the cost of living crisis tweets by topic
top_terms_by_topic_LDA(tweets, number_of_topics = 2)