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))
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1 Introduction
- What task do Pang, Lee and Vaithyanathan study, and how does it differ from traditional text classification?Classifying whole documents by overall sentiment (positive vs negative) rather than by topic. Topics are often identifiable by keywords alone, but sentiment can be expressed subtly: “How could anyone sit through this movie?” contains no single obviously negative word.
- 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)), §1, p. 79
- What applications of document-level sentiment classification did Pang et al. list?Summarizing reviews (labelling reviews without explicit ratings and normalizing rating schemes), business intelligence, recommender systems, processing free-form survey responses, and message filtering, e.g. recognizing “flames”.
- 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)), §1, p. 79
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/