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Sentiment Identification Using Maximum Entropy Analysis of Movie Reviews
| Content Provider | Semantic Scholar |
|---|---|
| Author | Mehra, Nipun Khandelwal, Shashikant Patel, Priyank |
| Copyright Year | 2002 |
| Abstract | Sentiment classification is one of the most challenging problems in Natural Language Processing. A sentiment classifier recognizes patterns of word usage between different classes and attempts to put unlabeled text into one of these categories in an unsupervised manner. Therefore, the attempt is to classify documents not by topic but by overall sentiment. We have used reviews of movies to train and test our classifier. Our system uses the Maximum Entropy method of unsupervised machine learning. We present our observations, assumptions, and results in this paper. We conclude by looking at the challenges faced and the road ahead. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://web.stanford.edu/class/archive/cs/cs276a/cs276a.1032/projects/reports/nmehra-kshashi-priyank9.pdf |
| Alternate Webpage(s) | https://web.stanford.edu/class/archive/cs/cs276a/cs276a.1032/projects/reports/nmehra-kshashi-priyank9.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
