Social emotion classification of short text via topic-level maximum entropy model

Yanghui RAO, Haoran XIE, Jun LI, Fengmei JIN, Fu Lee WANG, Qing LI

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

With the rapid proliferation of Web 2.0, the identification of emotions embedded in user-contributed comments at the social web is both valuable and essential. By exploiting large volumes of sentimental text, we can extract user preferences to enhance sales, develop marketing strategies, and optimize supply chain for electronic commerce. Pieces of information in the social web are usually short, such as tweets, questions, instant messages, messages, and news headlines. Short text differs from normal text because of its sparse word co-occurrence patterns, which hampers efforts to apply social emotion classification models. Most existing methods focus on either exploiting the social emotions of individual words or the association of social emotions with latent topics learned from normal documents. In this paper, we propose a topic-level maximum entropy (TME) model for social emotion classification over short text. TME generates topic-level features by modeling latent topics, multiple emotion labels, and valence scored by numerous readers jointly. The overfitting problem in the maximum entropy principle is also alleviated by mapping the features to the concept space. An experiment on real-world short documents validates the effectiveness of TME on social emotion classification over sparse words. Copyright © 2016 Elsevier B.V. A.
Original languageEnglish
Pages (from-to)978-986
JournalInformation & Management
Volume53
Issue number8
Early online dateMay 2016
DOIs
Publication statusPublished - Dec 2016

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Entropy
Electronic commerce
Supply chains
Labels
Marketing
Sales
Experiments

Citation

Rao, Y., Xie, H., Li, J., Jin, F., Wang, F. L., & Li, Q. (2016). Social emotion classification of short text via topic-level maximum entropy model. Information & Management, 53(8), 978-986.

Keywords

  • Topic-level maximum entropy model
  • Social emotion classification
  • Short-text analysis
  • Public opinion mining