NDLI: Supervised term weighting for sentiment analysis

Content Provider	IEEE Xplore Digital Library
Author	Nguyen, T.T. Kuiyu Chang Siu Cheung Hui
Copyright Year	2011
Description	Author affiliation: School of Computer Engineering, Nanyang Technological University, China (Nguyen, T.T.; Kuiyu Chang; Siu Cheung Hui)
Abstract	Vector space text classification is commonly used in intelligence applications such as email and conversation analysis. In this paper we propose a supervised term weighting scheme called tƒ × KL (term frequency Kullback-Leibler), which weights each word proportionally to the ratio of its document frequency across the positive and negative class. We then generalize tƒ × KL to effectively deal with class imbalance, which is very common in real world intelligence analysis. The generalized tƒ × KL weights each word according to the ratio of the positive and negative class conditioned word probabilities instead of the raw document frequencies. Results on four classification datasets show tƒ × KL to perform consistently better than the baseline tƒ ×idƒ and 4 other supervised term weighting schemes, including the recently proposed tƒ × rƒ (term frequency relevance frequency). The generalized tƒ × KL was found to be extremely robust in dealing with highly skewed class distributions, beating the second runner-up by more than 20% on a dataset that has only 10% positive training examples. The generalized tƒ × KL is thus an effective and robust term weighting scheme that can significantly improve binary classification performance in sentiment analysis and intelligence applications.
Starting Page	89
Ending Page	94
File Size	1579307
Page Count	6
File Format	PDF
ISBN	9781457700828
e-ISBN	9781457700859
DOI	10.1109/ISI.2011.5984056
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Communities Benchmark testing Educational institutions
Content Type	Text
Resource Type	Article

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