Comparison of semantic analysis approache

1. Sentiment Analysis of
Twitter Data
Presented By Team 5
Bhagyashree Deokar (bdeokar)
Milinda Sreenath (mrsreena)
Rahul Singhal (rsingha2)
Rohit Sharma (rsharma9)
Yogesh Birla (ydbirla)

2. Purpose of sentiment analysis
Why Twitter Data
Challenges of Using Twitter Data
Introduction

3. 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

4. Based on Recursive Neural Tensor Network
Uses Stanford Sentiment Bank
Example: “I love this movie.”
Recursive Deep Model

5. 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

6. Data Collection using Twitter API
Data Preprocessing
Execution of Algorithm on 1400 classified tweets
Our Work

7. 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

8. 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

9. Thank You!