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: Contribution to journalConference articlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)408-422
Number of pages15
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10177 LNCS
Publication statusPublished - 2017
Externally publishedYes
Event22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017 - Suzhou, China
Duration: 27 Mar 201730 Mar 2017


  • Emotion detection
  • Short text analysis
  • Topic model


Dive into the research topics of 'Supervised intensive topic models for emotion detection over short text'. Together they form a unique fingerprint.

Cite this