Abstract
In order to overcome data sparsity and expression diversity problems of short text and to improve the quality of clustering, this paper proposes a text feature enhancement method based on biterm topic model (BTM). First, we obtain the high frequency word matrix of underlying topic based on the extraction on the corpus using BTM and then strengthen the traditional vector space model (VSM) selectively with this matrix to reduce vector dimension and highlight the main features. Also, we propose a heat calculation equation combining with propagation characteristic and time effect of micro-blogs so that we can better demonstrate the evolution of a topic and analyze it. Experiments show that our method has achieved good results in improving the clustering quality and the heat calculation equation is also beneficial to the discovery and evolution of hot topics.
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Feng, J., Fang, Y. (2017). Research on Hot Topic Discovery Technology of Micro-blog Based on Biterm Topic Model. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-3969-0_27
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DOI: https://doi.org/10.1007/978-981-10-3969-0_27
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