1. Opinion Mining – Twitter

3. 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

4. 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

5. 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”

6. 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

7. 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 )

8. 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".

9. Polarity plot

10. Word cloud
• Most frequently used words associated to the
tweets emotions are represented in form of
word cloud

12. 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