Name
Abstract | ||
|---|---|---|
The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers' emotions evoked by social media materials. Compared to the classical sentiment analysis from writers' perspective, sentiment analysis of readers is sometimes more meaningful in social media. We propose two sentiment topic models to associate latent topics with evoked emotions of readers. The first model which is an extension of the existing Supervised Topic Model, generates a set of topics from words firstly, followed by sampling emotions from each topic. The second model generates topics from social emotions directly. Both models can be applied to social emotion classification and generate social emotion lexicons. Evaluation on social emotion classification verifies the effectiveness of the proposed models. The generated social emotion lexicon samples further show that our models can discover meaningful latent topics exhibiting emotion focus. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.ins.2013.12.059 | Inf. Sci. |
Keywords | Field | DocType |
social emotion mining,social emotion lexicon,social media service,evoked emotion,social media material,social media,sampling emotion,sentiment topic model,social emotion lexicon sample,social emotion classification,social emotion,emotion focus | Social emotions,Social media,Sentiment analysis,Microblogging,Emotion classification,Lexicon,Natural language processing,Artificial intelligence,Topic model,Mathematics | Journal |
Volume | ISSN | Citations |
266, | 0020-0255 | 54 |
PageRank | References | Authors |
1.47 | 31 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanghui Rao | 1 | 256 | 23.32 |
| Qing Li | 2 | 3222 | 433.87 |
| Xudong Mao | 3 | 105 | 10.64 |
| Liu Wenyin | 4 | 2531 | 215.13 |