Sentiment Analysis of

Twitter Data

Presented By Team 5

Bhagyashree Deokar (bdeokar)

Milinda Sreenath (mrsreena)

Rahul Singhal (rsingha2)

Rohit Sharma (rsharma9)

Yogesh Birla (ydbirla)

Purpose of sentiment analysis

Why Twitter Data

Challenges of Using Twitter Data

Introduction

Simplest Probabilistic Classifier

Based on Bayes Theorem

Strong(naïve) independence assumption between

words in document

Considers the frequency of each term in document

Multinomial Naïve Bayes Classifier

Based on Recursive Neural Tensor Network

Uses Stanford Sentiment Bank

Example: “I love this movie.”

Recursive Deep Model

Influence of special characters like “@”, “!” eliminated

Intelligence added for not recognizing single sentence as multiple sentences

Mapping of new words to closest existing words in tree bank

Our Contribution - Improvements in

Recursive Deep Model

Data Collection using Twitter API

Data Preprocessing

Execution of Algorithm on 1400 classified tweets

Our Work

Parameter/Algorithm

Multinomial Naive Bayes

Recursive Deep Model

Accuracy 77.03 % 81.6 %

Time of Execution

0.06 sec 45.96 sec

Result

Accuracy Time of Execution

Simplicity Ease of Model Learning

Multinomial Naïve Bayes Classifier

Recursive Deep Model

Considering logical relation between words, Recursive

Deep Model provides better accuracy than Multinomial

Naïve Bayes Classifier

Multinomial Naïve Bayes is simple, easy to train and has

less execution time

Recursive Deep Model can be enhanced to provide

multilingual support

Conclusion & Future Direction

Thank You!