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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!

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Sentiment analysis of twitter data

  • 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