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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5.3 Support Vector Machines
- How does an SVM classify documents in the two-class case?Training finds the separating hyperplane \(\vec{w}\) with the largest margin; the solution is \(\vec{w} = \sum_j \alpha_j c_j \vec{d}_j\) with \(c_j \in \{1, -1\}\) and \(\alpha_j \ge 0\) from the dual problem. Documents with \(\alpha_j > 0\) are support vectors; a test document is classified by which side of the hyperplane it falls on.
- 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)), §5.3, p. 82
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/