This document summarizes a student's bachelor's thesis on sentence level polarity detection of movie reviews in Bangla. The thesis aimed to collect Bangla movie review data, process it to predict sentiment at the sentence level. The student tested various machine learning classifiers like SVM and Naive Bayes on the preprocessed review data. SVM with stemming and unigram features achieved the best accuracy of 88% for sentiment classification. Future work could explore using part-of-speech tags and negation handling to improve results.

1. Sentence level polarity detection on
movie review text in Bangla
Supervised By:
Md. Hasan Hafizur Rahman
Lecturer
Dept. of CSE
Comilla University
Bachelor’s Thesis
Presented By:
Nayan Banik
ID: 1108003
Session: 2011-12
Dept. of CSE
Comilla University

2. Outline
• Motivation
• Problem Definition
• Goal
• Methodology
• Results and Evaluation
• Conclusion

3. Text data is pervasive

4. Knowledge can be extracted from text data

5. Sentiment Analysis

6. Confusion
Which one
to watch???
So many choicesSo many choices

7. So many reviews to check

8. Goal
Collect Data
Process
Predict

9. System Structure

10. System Organizaton

11. Pre-processing
Input Text Pre-processed Text
,
;

12. Main Processing
Text Segmentation (Tokenization)
Stemming of sentence

13. Main Processing (Contd.)
Tf-idf Vectorization

14. Classification

15. Classification

16. Results
• SVM with stemming as
preprocessing and
unigram as feature
provided the best
accuracy of 88%.

17. Conclusion
• SVM performs well in domain specific data.
• Bigram feature is not effective for NB.
• POS tag and negation will be considered in
future work.
• Other classifiers will be implemented on our
domain.

18. Thank You