Sentiment Analysis on Twitter Data

1. Sentiment Analysis for Twitter
Priyanka Bajaj priyanka.bajaj@students.iiit.ac.in
Kamal Gurala kamal.gurala@students.iiit.ac.in
Faraz Alam faraz.alam@students.iiit.ac.in
Ritesh Kumar Gupta ritesh.kumar.gupta@in.ibm.com
Guided By : Satarupa Guha satarupaguha11@gmail.com

2. Agenda
1.Introduction – Sentiment Analysis
2.About Twitter and Our Goal
3.Glossary
4.Challenges
5.Approach
6.Results and Conclusion
7.Tools and Technologies

3. What is Sentiment Analysis?
Mechanism to extract opinions, emotions and sentiments in text
Enable us to track attitudes and feelings on the web based on blog posts, comments, reviews
and tweets on different topics
Enable to track products, brands and people and determine whether they are viewed positively
or negatively on the web.
acts: "The painting was more expensive than a Monet"
pinions: "I honestly don't like Monet, Pollock is better”

4. • An online social networking and micro blogging service
• Enables users to send and read "tweets", which are text messages limited to 140 characters,
hence unambiguous
• 500 million tweets daily by 240+ million active users
• Audience varies from common man to celebrities
• Users discuss current affairs and share personal views
Our goal:
To determine whether the expressed opinion in the tweets is positive, negative or neutral.
For tweets conveying both a positive and negative sentiment, choose the stronger sentiment
About

5. Natural Language Processing: The attempt to use programming to read and understand the
meaning of text.
Semantic Analysis:
Use of Natural Language processing (NLP) to derive "sentiment," or subjective information
from text.
Artificial Intelligence:
Using information provided by NLP and mathematics to determine whether something is
negative or positive
Glossary

6. Challenges
• Tweets are highly unstructured and also non-grammatical
• Out of Vocabulary Words
• Lexical Variation
• Extensive usage of acronyms like asap, lol, afaik

7. Our System

8. • Tweet Downloader
– Download the tweets using Twitter API
• Tokenisation
– Twitter specific POS Tagger and tokenizer developed by ARK Social Media Search
• Preprocessing
– Replacing Emoticons by their polarity, assign scores
– Remove URL, Target Mentions
– Replace #text -> text, since hashtags may contribute to the sentiment
– Replace Sequence of Repeated Characters eg. ‘cooooool’ by ‘cool’ and assign higher
score
– Twitter specific stop word removal
– Acronym expansion
System Details

9. • Feature Extractor
– Unigrams and Bigrams
– Polarity Score of the Tweet (f1)
– Count of Positive/Negative Words (f2,f3)
– Maximum Positive/Negative Score for Words (f4,f5)
– Count of Positive/Negative Emoticons and assign scores(contibutes to all f1,f2,f3,f4,f5)
– Positive/Negative special POS Tags Polarity Score
• Classifier and Prediction
– Features extracted are fed into to SVM classifier
– Model built used to predict sentiment of new tweets
System Details Contd.

10. Results and Conclusion
A baseline model by taking the unigrams, and compare it with the bigrams and
lexicon features model
Sub-Task Baseline Model Feature Based
Model
Sentence Based 49.81% 57.85%
Accuracy F1 Score (f-Measure)
Sub-Task Baseline Model Feature Based
Model
Sentence Based 55.56 61.17
• We investigated two kinds of models: Baseline and Feature Based Models
• For our feature-based approach, feature analysis reveals that the most important features
are bigrams and those that combine the prior polarity of words and their parts-of-speech
tags

11. 1. Concepts of Data Mining and Information Retrieval
2. Python Language
3. Java, Eclipse
4. Support Vector Machine(SVM) Theory
5. LIBSVM package for accuracy and f-Measure
6. Twitter.inc API for training set
7. NLTK
8. Shell Script for integration
Tools and Technology Used

12. Thank You