With the emergence of social media services, documents that only include a few words are becoming increasingly prevalent. More and more users post short messages to express their feelings and emotions through Twitter, Flickr, YouTube and other apps. However, the sparsity of word co-occurrence patterns in short text brings new challenges to emotion detection tasks. In this paper, we propose two supervised intensive topic models to associate latent topics with emotional labels. The first model constrains topics to relevant emotions, and then generates document-topic probability distributions. The second model establishes association among biterms and emotions by topics, and then estimates word-emotion probabilities. Experiments on short text emotion detection validate the effectiveness of the proposed models. Copyright © 2017 Springer International Publishing AG.
|Title of host publication||Database systems for advanced applications: 22nd International Conference, DASFAA 2017, Suzhou, China, March 27-30, 2017, proceedings, part I|
|Editors||Selçuk CANDAN, Lei CHEN, Torben Bach PEDERSEN, Lijun CHANG, Wen HUA|
|Place of Publication||Cham|
|ISBN (Print)||9783319557526, 9783319557533|
|Publication status||Published - 2017|
CitationRao, Y., Pang, J., Xie, H., Liu, A., Wong, T.-L., Li, Q., et al. (2017). Supervised intensive topic models for emotion detection over short text. In S. Candan, L. Chen, T. B. Pedersen, L. Chang, & W. Hua (Eds.), Database systems for advanced applications: 22nd International Conference, DASFAA 2017, Suzhou, China, March 27-30, 2017, proceedings, part I (pp. 408-422). Cham: Springer.
- Topic model
- Emotion detection
- Short text analysis