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

Zhenyan Liu; Yueshi Qiu; Zhe Zhang; Limin Mao; Xiaohan Zheng

doi:10.1088/1742-6596/1288/1/012046

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

Zhenyan Liu, Yueshi Qiu, Zhe Zhang, Limin Mao, Xiaohan Zheng

计算机学院

科研成果: 期刊稿件 › 会议文章 › 同行评审

1 引用（Scopus）

摘要

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.

源语言	英语
文章编号	012046
期刊	Journal of Physics: Conference Series
卷	1288
期	1
DOI	https://doi.org/10.1088/1742-6596/1288/1/012046
出版状态	已出版 - 15 8月 2019
活动	5th Annual International Conference on Network and Information Systems for Computers, ICNISC 2019 - Wuhan, 中国期限: 19 4月 2019 → 20 4月 2019

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Liu, Z., Qiu, Y., Zhang, Z., Mao, L., & Zheng, X. (2019). Research on Hot Topics Discovery Based on Short Texts of Online Reviews. Journal of Physics: Conference Series, 1288(1), 文章 012046. https://doi.org/10.1088/1742-6596/1288/1/012046

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abstract = "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.",

author = "Zhenyan Liu and Yueshi Qiu and Zhe Zhang and Limin Mao and Xiaohan Zheng",

note = "Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 5th Annual International Conference on Network and Information Systems for Computers, ICNISC 2019 ; Conference date: 19-04-2019 Through 20-04-2019",

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AU - Liu, Zhenyan

AU - Qiu, Yueshi

AU - Zhang, Zhe

AU - Mao, Limin

AU - Zheng, Xiaohan

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.

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DO - 10.1088/1742-6596/1288/1/012046

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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 -

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

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