Research on Hot Topics Discovery Based on Short Texts of Online Reviews

The two crucial issues for hot topics discovery based on online reviews are that the sparsity of short text features and the "long tail" phenomenon of hot topics. This paper focuses on these two key issues, and proposes an improved similarity calculation method to calculate the similarity...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-08, Vol.1288 (1), p.12046
Main Authors: Liu, Zhenyan, Qiu, Yueshi, Zhang, Zhe, Mao, Limin, Zheng, Xiaohan
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Mathematical analysis
Outliers (statistics)
Physics
Similarity
Texts
Time factors
Citations: Items that this one cites
Online Access:Get full text
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Summary:The two crucial issues for hot topics discovery based on online reviews are that the sparsity of short text features and the "long tail" phenomenon of hot topics. This paper focuses on these two key issues, and proposes an improved similarity calculation method to calculate the similarity of short texts, and a novel clustering algorithm based on the time factor and dynamic adjustment of comparison times to automatically discard a large number of outliers. Moreover, the validity and advancement of the new method are presented by comparative experiments using real data sets.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1288/1/012046