Abstract
It is a critical task to infer discriminative and coherent topics from short texts. Furthermore, people not only want to know what kinds of topics can be extract from these short texts, but also desire to obtain the temporal dynamic evolution of these topics. In this paper, we present a novel model for short texts, referred as topic trend detection (TTD) model. Based on an optimized topic model we proposed, TTD model derives more typical terms and itemsets to represent topics of short texts and improves the coherence of topic representations. Ultimately, we extend the topic itemsets obtained from the optimized topic model by word embeddings to detect topic trends. Through extensive experiments on several real-world short text collections in Sina Microblog, the result demonstrate our method achieves comparable topic representations than state-of-the-art models, measured by topic coherence, and then show its application in identifying topic trends in Sina Microblog.
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Notes
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In the following paper, the event name on microblog will be replaced by English to avoid the Chinese problems in Tex.
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Acknowledgement
This work is funded by the National Natural Science Foundation of China under Grant No. 61472329, No. 61532009 and the Innovation Fund of Xihua University. We would like to thank the anonymous reviewers for their helpful comments.
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He, L., Du, Y., Ye, Y. (2017). Tracking Topic Trends for Short Texts. In: Li, J., Zhou, M., Qi, G., Lao, N., Ruan, T., Du, J. (eds) Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence. CCKS 2017. Communications in Computer and Information Science, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-10-7359-5_12
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