Vector Representation of Words for Detecting Topic Trends over Short Texts

DOI

10.2991/mmsa-18.2018.97How to use a DOI?

Keywords

topic model; short text; vector space representations; trend detection

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 vector space representations of words to detect topic trends. Through extensive experiments on several real-world short text collections in Sina Microblog, the results show 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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liyan He
AU  - Yajun Du
AU  - Lei Zhang
PY  - 2018/03
DA  - 2018/03
TI  - Vector Representation of Words for Detecting Topic Trends over Short Texts
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 436
EP  - 442
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.97
DO  - 10.2991/mmsa-18.2018.97
ID  - He2018/03
ER  -