Supervised intensive topic models for emotion detection over short text

Yanghui RAO, Jianhui PANG, Haoran XIE, An LIU, Tak Lam WONG, Qing LI, Fu Lee WANG

Research output: Chapter in Book/Report/Conference proceedingChapters

5 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationDatabase systems for advanced applications: 22nd International Conference, DASFAA 2017, Suzhou, China, March 27-30, 2017, proceedings, part I
EditorsSelçuk CANDAN, Lei CHEN, Torben Bach PEDERSEN, Lijun CHANG, Wen HUA
Place of PublicationCham
PublisherSpringer
Pages408-422
ISBN (Print)9783319557526, 9783319557533
DOIs
Publication statusPublished - 2017

Citation

Rao, 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.

Keywords

  • Topic model
  • Emotion detection
  • Short text analysis

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