The rapid development of social media services has facilitated the communication of opinions through microblogs/tweets, instantmessages, 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.
|Title of host publication||Database Systems for Advanced Applications - DASFAA 2015 International Workshops|
|Editors||Yoshiharu ISHIKAWA, Sarana NUTANONG, An LIU, Tieyun QIAN, Muhammad Aamir CHEEMA|
|Place of Publication||Switzerland|
|Number of pages||10|
|Publication status||Published - 2015|
|Event||The 20th International Conference on Database Systems for Advanced Applications - Hanoi, Viet Nam|
Duration: 20 Apr 2015 → 23 Apr 2015
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||The 20th International Conference on Database Systems for Advanced Applications|
|Abbreviated title||DASFAA 2015|
|Period||20/04/15 → 23/04/15|
Bibliographical noteThe 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).
- Intensive maximum entropy model
- Sentiment classification
- Short text analysis