Intensive maximum entropy model for sentiment classification of short text
Author(s)
Xie, Haoran
Author(s)
Rao, Y.
Li, J.
Xiang, X.
Date Issued
2015
Publisher
Springer
Related Publication(s)
Database Systems for Advanced Applications (DASFAA 2015 International Workshops) Revised Selected Papers
Start page
42
End page
51
Abstract
The rapid development of social media services has facilitated the communication of opinions through microblogs/tweets, instant-messages, online news, and so forth. This article concentrates on the mining of emotions evoked by short text materials. Compared to the classical sentiment analysis from long text, sentiment analysis of short text is sometimes more meaningful in social media. We propose an intensive maximum entropy model for sentiment classification, which generates the probability of sentiments conditioned to short text by employing intensive feature functions. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of short text.
SFU Affiliated Publication
Yes
Availability at SFU Library
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