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.
Bibliographical noteThe research described in this paper was substantially supported by the National Natural Science Foundation of China (No. 61502545), “the Fundamental Research Funds for the Central Universities” (No. 46000-31610009), and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14). This work has also been supported, in part, by a Strategic Research Grant (Project no. 7004218) and an Applied Research Grant (Project no. 9667095), both of City University of Hong Kong. This paper is an extended version of our previous conference paper.
- Public opinion mining
- Short-text analysis
- Social emotion classification
- Topic-level maximum entropy model