Open AccessResearch on Hot Topics Discovery Based on Short Texts of Online Reviews
Author(s) -
Zhenyan Liu,
Yueshi Qiu,
Zhe Zhang,
Limin Mao,
Xiaowei Zheng
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1288/1/012046
Subject(s) - similarity (geometry) , computer science , outlier , cluster analysis , key (lock) , data mining , information retrieval , data science , artificial intelligence , image (mathematics) , computer security
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.