TY - JOUR
T1 - Research on Hot Topics Discovery Based on Short Texts of Online Reviews
AU - Liu, Zhenyan
AU - Qiu, Yueshi
AU - Zhang, Zhe
AU - Mao, Limin
AU - Zheng, Xiaohan
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85072115974&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1288/1/012046
DO - 10.1088/1742-6596/1288/1/012046
M3 - Conference article
AN - SCOPUS:85072115974
SN - 1742-6588
VL - 1288
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012046
T2 - 5th Annual International Conference on Network and Information Systems for Computers, ICNISC 2019
Y2 - 19 April 2019 through 20 April 2019
ER -