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AN INTERNET REVIEW TOPIC HIERARCHY MINING METHOD BASED ON MODIFIED CONTINUOUS RENORMALIZATION PROCEDURE

LIN QI

https://orcid.org/0000-0002-5816-8127

School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, P. R. China

E-mail Address: qilin@bistu.edu.cn

Corresponding authors.

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FEI-YAN GUO

School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, P. R. China

Beijing World Urban Circular Economy System (Industry), Collaborative Innovation Center, Beijing 100192, P. R. China

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JIAN ZHANG

School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, P. R. China

Beijing International Science and Technology Cooperation, Base of Intelligent Decision and Big Data Application, Beijing 100192, P. R. China

E-mail Address: zhangjian@bistu.edu.cn

Corresponding authors.

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, and

YU-WEI WANG

Department of East Asian Studies, University of Arizona, Tucson AZ85719, USA

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https://doi.org/10.1142/S0218348X22501341Cited by:0 (Source: Crossref)

Abstract

Mining the hierarchical structure of Internet review topics and realizing a fine classification of review texts can help alleviate users’ information overload. However, existing hierarchical topic classification methods primarily rely on external corpora and human intervention. This study proposes a Modified Continuous Renormalization (MCR) procedure that acts on the keyword co-occurrence network with fractal characteristics to achieve the topic hierarchy mining. First, the fractal characteristics in the keyword co-occurrence network of Internet review text are identified using a box-covering algorithm for the first time. Then, the MCR algorithm established on the edge adjacency entropy and the box distance is proposed to obtain the topic hierarchy in the keyword co-occurrence network. Verification data from the Dangdang.com book reviews shows that the MCR constructs topic hierarchies with greater coherence and independence than the HLDA and the Louvain algorithms. Finally, reliable review text classification is achieved using the MCR extended bottom-level topic categories. The accuracy rate ( $P)$ $P)$ , recall rate ( $R)$ $R)$ and $F$ $F$ 1 value of Internet review text classification obtained from the MCR-based topic hierarchy are significantly improved compared to four target text classification algorithms.

Keywords:

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