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Enhancing Negation-Aware Sentiment Classification on Product Reviews via Multi-Unigram Feature Generation

Wei Wei ; Jon Atle Gulla ; Zhang Fu (Institutionen för data- och informationsteknik, Nätverk och system, Datakommunikation och distribuerade system (Chalmers))
Lecture Notes in Computer Science. 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, 18-21 August 2010 (0302-9743). Vol. 6215 (2010), p. 380-391.
[Konferensbidrag, refereegranskat]

Sentiment classification on product reviews has become a popular topic in the research community. In this paper, we propose an approach to generating multi-unigram features to enhance a negation-aware Naive Bayes classifier for sentiment classification on sentences of product reviews. We coin the term "multi-unigram feature" to represent a new kind of features that are generated in our proposed algorithm with capturing high-frequently co-appeared unigram features in the training data. We further make the classifier aware of negation expressions in the training and classification process to eliminate the confusions of the classifier that is caused by negation expressions within sentences. Extensive experiments on a human-labeled data set not only qualitatively demonstrate good quality of the generated multi-unigram features but also quantitatively show that our proposed approach beats three baseline methods. Experiments on impact analysis of parameters illustrate that our proposed approach stably outperforms the baseline methods.

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Denna post skapades 2011-01-05. Senast ändrad 2012-02-14.
CPL Pubid: 132435


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