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As a part of Twitter Data Analysis, So far I have completed Movie review using R& Document Classification using R**. **Today we will be dealing with discovering topics in Tweets, i.e. to mine the tweets data to discover underlying topics– approach known as Topic Modeling.

A statistical approach for discovering “abstracts/topics” from a collection of text documents based on statistics of each word. In simple terms, the process of looking into a large collection of documents, identifying clusters of words and grouping them together based on similarity and identifying patterns in the clusters appearing in multitude.

Consider the below Statements:

- I love playing cricket.
- Sachin is my favorite cricketer.
- Titanic is heart touching movie.
- Data Analytics is next Future in IT.
- Data Analytics & Big Data complements each other.

When we apply Topic Modeling to the above statements, we will be able to group statement **1&2 **as **Topic-1 **(later we can identify that the topic is **Sport**)**,**statement** 3 **as** Topic-2 **(topic is **Movies**)**, **statement** 4&5 **as** Topic-3 **(topic is**data Analytics**).

fig: Identifying topics in Documents and classifying as Topic 1 & Topic 2

Topic Modeling can be achieved by using Latent Dirichlet Allocation algorithm. Not going into the nuts & bolts of the Algorithm, LDA automatically learns itself to assign probabilities to each & every word in the Corpus and classify into Topics. A simple explanation for LDA could be found here:

Steps Involved:

- Fetch tweets data using ‘
**twitteR**’ package. - Load the data into the R environment.
- Clean the Data to remove: re-tweet information, links, special characters, emoticons, frequent words like is, as, this etc.
- Create a Term Document Matrix (TDM) using ‘
**tm’**Package. - Calculate TF-IDF i.e. Term Frequency Inverse Document Frequency for all the words in word matrix created in Step 4.
- Exclude all the words with tf-idf <= 0.1, to remove all the words which are less frequent.
- Calculate the optimal Number of topics (K) in the Corpus using log-likelihood method for the TDM calculated in Step6.
- Apply LDA method using
**‘topicmodels’**Package to discover topics. - Evaluate the model.

Topic modeling using LDA is a very good method of discovering topics underlying. The analysis will give good results if and only if we have large set of Corpus.In the above analysis using tweets from top 5 Airlines, I could find that one of the topics which people are talking about is about **FOOD **being served. We can Sentiment Analysis techniques to mine what people thinks about, talks about products/companies etc.

**SourceCode:**

library("tm")

library("wordcloud")

library("slam")

library("topicmodels")

#Load Text

con <- file("tweets.txt", "rt")

tweets = readLines(con)

#Clean Text

tweets = gsub("(RT|via)((?:\\b\\W*@\\w+)+)","",tweets)

tweets = gsub("http[^[:blank:]]+", "", tweets)

tweets = gsub("@\\w+", "", tweets)

tweets = gsub("[ \t]{2,}", "", tweets)

tweets = gsub("^\\s+|\\s+$", "", tweets)

tweets <- gsub('\\d+', '', tweets)

tweets = gsub("[[:punct:]]", " ", tweets)

corpus = Corpus(VectorSource(tweets))

corpus = tm_map(corpus,removePunctuation)

corpus = tm_map(corpus,stripWhitespace)

corpus = tm_map(corpus,tolower)

corpus = tm_map(corpus,removeWords,stopwords("english"))

tdm = DocumentTermMatrix(corpus) # Creating a Term document Matrix

# create tf-idf matrix

term_tfidf <- tapply(tdm$v/row_sums(tdm)[tdm$i], tdm$j, mean) * log2(nDocs(tdm)/col_sums(tdm > 0))

summary(term_tfidf)

tdm <- tdm[,term_tfidf >= 0.1]

tdm <- tdm[row_sums(tdm) > 0,]

summary(col_sums(tdm))

#Deciding best K value using Log-likelihood method

best.model <- lapply(seq(2, 50, by = 1), function(d){LDA(tdm, d)})

best.model.logLik <- as.data.frame(as.matrix(lapply(best.model, logLik)))

#calculating LDA

k = 50;#number of topics

SEED = 786; # number of tweets used

CSC_TM <-list(VEM = LDA(tdm, k = k, control = list(seed = SEED)),VEM_fixed = LDA(tdm, k = k,control = list(estimate.alpha = FALSE, seed = SEED)),Gibbs = LDA(tdm, k = k, method = "Gibbs",control = list(seed = SEED, burnin = 1000,thin = 100, iter = 1000)),CTM = CTM(tdm, k = k,control = list(seed = SEED,var = list(tol = 10^-4), em = list(tol = 10^-3))))

#To compare the fitted models we first investigate the values of the models fitted with VEM and estimated and with VEM and fixed

sapply(CSC_TM[1:2], slot, "alpha")

sapply(CSC_TM, function(x) mean(apply(posterior(x)$topics, 1, function(z) - sum(z * log(z)))))

Topic <- topics(CSC_TM[["VEM"]], 1)

Terms <- terms(CSC_TM[["VEM"]], 8)

Terms

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