This document discusses sentiment analysis on Twitter data using machine learning classifiers. It describes Twitter sentiment analysis as determining if a tweet is positive, negative, or neutral. Some challenges are that people express opinions complexly using sarcasm, irony, and slang. The document tests different classifiers like Naive Bayes and SVM on Twitter data preprocessed by tokenizing, extracting sentiment features, and part-of-speech tagging. It finds that extracting more features like sentiment and part-of-speech tags along with an SVM classifier achieves the best accuracy of 68% at determining tweet sentiment.

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