Information Retrieval and Extraction Major Project 2016 IIIT Hyderabad

1. TWITTER SENTIMENT
ANALYSIS
By:
Ayush Khandelwal
Goutam Nair
Pravallika Rao
Course: Information Retrieval and Extraction
IIIT Hyderabad
Under the guidance of Prof. Vasudeva Varma

2. Problem Statement
 Input - Textual content of a tweet
 Output – Label signifying the sentiment of the tweet (Positive, Neutral or
Negative)

3. Motivation
 Tweets sometimes express opinions about different topics. These opinions are
important
 Consumers can use sentiment analysis to research products or services before
making a purchase. E.g. Kindle
 Marketers can use this to research public opinion of their company and products,
or to analyze customer satisfaction. E.g. Election Polls
 Organizations can also use this to gather critical feedback about problems in newly
released products. E.g. Brand Management (Nike, Adidas)

4. Challenges
 Noisy text
 Lack of context - 140 characters only
 Acronyms - lol, brb, gr8
 Emoticons - :) , :( , :|
 Negation

5. Approach
Tweet
Downloader
Parser
Pre-
processing
Feature
Extractor
Add
Additional
Features
SVM
Classifier &
Prediction

6. Approach
 Tweet Downloader
 Download the tweets using twitter API
(https://github.com/aritter/twitter_download).
 9684 training and 8987 testing tweets are downloaded.
 Parser
 The parser removes all unavailable tweets from the downloaded data
 After removing these we have 7612 tweets for training and 7868 tweets
for testing

7. Approach
 Pre-processing
 Replace Emoticons by their polarity.
 Remove URLs and Targets.
 Expand acronyms. eg 'brb' to 'be right back'
 Remove stop words.
 Tokenization
 Stemming
 Case-folding
 Remove punctuation marks
 Replace sequence of repeating characters eg. 'hellooooo' by 'helloo'

8. Approach
 Feature Extractor
 The pre-processed data file is fed to the feature extractor which creates the
feature vector.
 The basic(baseline) feature that was considered was of unigrams.
 A list of all unique unigrams across the training set was constructed and it formed
the basic vector for each tweet.
 Synsets are used for words that are not found in the list of unique unigrams.

9. Approach
 Add Additional Features
 Polarity scores of the tweets
 Negation
 Hashtags
 Special characters (?,!,*)
 Capitalized words
 SVM Classification and Prediction
 The features extracted are passed to the classifier
 The model built is used to predict the sentiment of the new tweets

10. Results
Features Accuracy Precision Recall F1 score
Unigram 54.855% 0.5264 0.5061 0.5126
Unigram+Additional
features
57.079% 0.5525 0.5308 0.5386
Bigrams 58.579% 0.5713 0.5173 0.5269
Bigrams+Additional
features
60.739% 0.5930 0.5525 0.5637

11. Links
 Github Repositary - https://github.com/ayush-khandelwal7/Twitter-
Sentiment-Analysis
 Github Page - http://goutamnair7.github.io/Twitter-Sentiment-Analysis