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.
Jun Li—The research work described in this article has been substantially supported by “the Fundamental Research Funds for the Central Universities” (Project Number: 46000-31610009).
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Acknowledgements
The authors are thankful to the anonymous reviewers for their constructive comments and suggestions on an earlier version of this paper. The research described in this paper has been supported by “the Fundamental Research Funds for the Central Universities” (46000-31121401), and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14).
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Rao, Y., Li, J., Xiang, X., Xie, H. (2015). Intensive Maximum Entropy Model for Sentiment Classification of Short Text. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_4
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DOI: https://doi.org/10.1007/978-3-319-22324-7_4
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