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Uploaded bySandhiya Kothandan
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Opinion Mining – Twitter

This document discusses performing sentiment analysis on tweets collected from Twitter. It describes defining sentiment, collecting Twitter data using API keys, cleaning the tweet data, scoring sentiment by comparing words to lexicons, classifying emotions and polarity using naive Bayes algorithms, and visualizing results with polarity plots and word clouds. The analysis is deployed using an R Shiny app.

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Opinion Mining – Twitter
Sentiment Definition • Sentiment means “a general thought, view, feeling, emotion, opinion, or sense,” Sentiment analysis - “the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in source materials.” • To perform a sentiment analysis about some event, we need to teach computers what a sentiment is (i.e., how to define “positive” or “negative” and “good” or “bad”). This is where machine learning comes in: we must teach computers the meaning of positive, negative, and so on
SENTIMENT DATA • Data is fetched from twitter for this project Downloading Data From Social API: • Need to register your app in twitter apps • Get Access token ,Access token secret , consumer key , consumer secret • Save the data to run along with your code to fetch data
Cleaning the data • Remove html link , re tweet entities , remove hastag , @ , punctuation , numbers , white spaces and slang words • Convert the collected tweets to lower case • Remove NA and repeated tweets • I have written a function to perform all this “cleanuptweets.R”
Scoring the Sentiment • To score the tweets I have tokenized the sentence into words and then check these words against a list of positive and negative words. • opinion lexicon Positive and Negative list of words are downloaded and compared to the tweets • My score.sentiment(), computes the raw sentiment based on the simple matching algorithm – does boolean match to the list already present and sums the scores as total positive or negative • Return a data frame with respective sentence and the score
Naives Bayes – Emotion • Naive Bayes is a probabilistic model that is unsurprisingly built upon a naive interpretation of Bayesian statistics • Naive Bayes is a linear classifier • For texts - we use a probabilistic method such as the NBC. • Classify-emotion() function in sentiment package is used (Anger , disgust , fear , joy sadness , surprise )
Naïve Bayes – Polarity • classify_polarity function is used to classify the polarity of the words in the tweets in built in Sentiment packages • Say positive , Negative or neutral • The idea is to compute the log likelihood of a tweet, assuming it belongs to either of the two classes. Once these likelihoods are calculated, a ratio of the pos-likelihood to neg-likelihood is calculated, and, based on this ratio, the tweets are classified as belonging to a particular class. It's important to note that if this ratio turns out to be 1, then the overall sentiment of the tweet is assumed to be "neutral".
Polarity plot
Word cloud • Most frequently used words associated to the tweets emotions are represented in form of word cloud
Deployment • I have used Rshiny app for deployment • Shiny has better visualization • Ui.r and Server.r are the two files needed . • Just three lines of code to run the program for users who are not familiar with R • Code can be found in my github link