Thumbs up? Sentiment Classification using Machine Learning Techniques
Pang et al., 2002, Thumbs up? Sentiment Classification using Machine Learning Techniques (Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002))
Subscriber deck — free during early access
2 cards in “3 The Movie-Review Domain” · whole deck
3 The Movie-Review Domain
- Why are movie reviews convenient for sentiment classification experiments?Large online collections exist, and reviewers often summarize their sentiment with a machine-extractable rating (stars or a number), so no hand labelling is needed for training or evaluation.
- Pang et al., 2002, Thumbs up? Sentiment Classification using Machine Learning Techniques (Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)), §3, p. 80
- How was the movie-review corpus built?From the IMDb archive of the rec.arts.movies.reviews newsgroup: reviews with star or numeric ratings, automatically mapped to positive, negative or neutral (only positive vs negative used). Fewer than 20 reviews per author per category were kept so prolific reviewers wouldn’t dominate: 752 negative and 1301 positive reviews from 144 reviewers, released publicly.
- Pang et al., 2002, Thumbs up? Sentiment Classification using Machine Learning Techniques (Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)), §3, p. 80
Source
- Bo Pang, Lillian Lee and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment Classification using Machine Learning Techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 79–86. https://aclanthology.org/W02-1011/