As a part of Twitter Data Analysis, So far I have
completed Movie
review using R & Document
Classification using R

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.

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:
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

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

**.**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.**What is 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

**Latent Dirichlet Allocation algorithm (LDA):**

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:

**Twitter Data Analysis Using LDA:**

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.

**Conclusion:**

**FOOD**being served. We can Sentiment Analysis techniques to mine what people thinks about, talks about products/companies etc.

**SourceCode:**

library("tm")

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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