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Social Network Analysis and Mining

Erschienen in:

01.12.2020 | Original Article

Topics extraction in incremental short texts based on LSTM

verfasst von: Xubo Zhang, Li Zhang

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

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Abstract

With the development of online social media, the topic extraction of short text has become an important research field. How to extract the topic, especially new topics that have not been recognized, from increasing and updated short texts has attracted the attention of scholars. This paper focuses on constructing a system based on long short-term memory (LSTM) model in deep learning. Firstly, the short text is converted to a word vector matrix by the word2vec model. After that, two models based on LSTM were designed. One is used to recognize whether the text belongs to an existing topic or a new one. The other identifies whether two text samples belong to the same topic or not. Finally, a hierarchical clustering model is used to find the number of new topics based on the output information of the two LSTM models. The experimental results show that the system constructed in this paper can identify new text topics well and achieve good algorithm performance.

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Titel: Topics extraction in incremental short texts based on LSTM
verfasst von: Xubo Zhang
Li Zhang
Publikationsdatum: 01.12.2020
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-020-00699-8

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