Supervised intensive topic models for emotion detection over short text
Author(s)
Xie, Haoran
Wang, Philips Fu Lee
Wong, Tak Lam
Author(s)
Rao, Y.
Pang, J.
Liu, A.
Li, Q.
Date Issued
2017
Publisher
Springer
Related Publication(s)
Database Systems for Advanced Applications (22nd International Conference, DASFAA 2017) Proceedings, Part I
Start page
408
End page
422
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.
SFU Affiliated Publication
Yes
Availability at SFU Library
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