###@Author Shivam Sharma(28shivamsharma@gmail.com) DataSet is similar to dataset used by Pang et. al.[1] in their research. You can directly download the data from the authors page. References related to my project is given below. Kindly Let me know if data is removed or page not available problems occurs.
How do I Run this program?
- First install R and packages listed below:-
- stringr
- openNLP
- NLP
- e1071
- NLP
- stringi
-
Running program:-
Run code(40-features).R program on R platform for getting accuracy of
71%(approx). Features here is bag of words. Then finally for prediction run 3-fold.R or 5-fold.R program.
###Codes:- Codes are written in R language with names showing its approaches. Let us take this program code(40-features).R means the code is using 40 most informative features as a model. Similarly with others. ###Bootstrapping Code is use for increasing my features by use of wordnet package. ###Cross Validation 3-fold & 5-fold codes are use for cross validation. In 3-fold.R program Support Vector machine is used but in 5-fold.R Naive bayes is used. You can change according to your convenient. ###Stemming Stemming for reducing words to their stems(Continued ==> continu & Continue ==> continu).
###References:- [1] B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?Sentiment classification using machine learnin techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 79-86, 2002.
[2] R. Feldman. Techniques and applications for sentiment analysis. Published in: Magazine Communications of the ACM Volume 56 Issue 4, April 2013 Pages 82-89.