IRE Project presentation on Twitter Sentiment analysis.

1. Twitter Sentiment Analysis
Akhil Batra
Avinash Kalivarapu
Sunil Kandari

2. Sentiment Analysis ?
• Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or
neutral.
• Also referred to as opinion mining, it makes our goal to determine whether the data(tweet) is
positive, negative or neutral.

3. Why is Sentiment Analysis Important?
• In public opinions eg:
• Is this product review positive or negative?
• Is this customer email satisfied or dissatisfied?
• Based on a sample of tweets, how are people responding to this ad campaign/product
release/news item?
• How have bloggers' attitudes about the president changed since the election?

4. Why Twitter Data for Sentiment Analysis?
• Popular microblogging site
• Short Text Messages of 140 characters
• 240+ million active users
• 500 million tweets are generated everyday
• Twitter audience varies from common man to celebrities
• Users often discuss current affairs and share personal views on
• various subjects
• Tweets are small in length and hence unambiguous

5. Problem Statement
Given a message, decide whether the message is of positive, negative, or neutral
sentiment. For messages conveying both a positive and negative sentiment,
whichever is the stronger sentiment should be chosen

6. Challenges
• People express opinions in complex ways
• In opinion texts, lexical content alone can be misleading
• Intra-textual and sub-sentential reversals,negation, topic change common
• Rhetorical devices/modes such as sarcasm, irony, implication, etc.
• Unstructured and also non-grammatical
• Lexical Variation
• Out of Vocabulary Words
• Extensive usage of acronyms like asap, lol, afaik

7. Twitter Dataset
Preprocessing
Tokenizer
Feature Extraction (Word +Senti Feature)
Classification(unigram-bigram SVM/Bayes )
Process Flow

8. Training

9. Testing

10. Extracted Features
• Word feature
• Word polarity score using wordnet
• Positive/Negative Hash Tags
• Positive/Negative/Extremely Positive/Extremely Negative Emoticons
• Negations
• POS tag polarity score(Noun,Preposition,Adjectives)
• Special characters
• Count of repetition words
• Count of Non English words
• Count of Acronyms

11. Classifiers
• Naive Bayes Classifier
• SVM

12. Analysis and Results
Classifiers % Accuracy
Unigram + Bayes Classification function 50*
Bigram + Bayes Classification function 54*
Unigram + SVM 65*
Unigram+ Senti-Feature+SVM 66*
Unigram+ Senti-Feature+POS polarity+SVM 68*

14. Conclusion
We conclude that extracting features and POS tagging of tweets gives the best
result using SVM classifier
There is always a scope of increasing the accuracy by extracting more features
which are relevant for the sentiments
Increasing the n-gram value to more than 2 does not necessarily increase the
accuracy