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.
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Acknowledgements
We are grateful to the anonymous reviewers for their valuable comments on this manuscript. The research has been supported by the National Natural Science Foundation of China (61502545, 61572336), two grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16 and UGC/FDS11/E06/14), the Start-Up Research Grant (RG 37/2016-2017R), and the Internal Research Grant (RG 66/2016-2017) of The Education University of Hong Kong.
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Rao, Y. et al. (2017). Supervised Intensive Topic Models for Emotion Detection over Short Text. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_26
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DOI: https://doi.org/10.1007/978-3-319-55753-3_26
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