Topic Network Analysis Based on Co-Occurrence Time Series Clustering

Weibin Lin, Xianli Wu, Zhengwei Wang, Xiaoji Wan () and Hailin Li
Additional contact information
Weibin Lin: College of Business Administration, Huaqiao University, Quanzhou 362021, China
Xianli Wu: College of Business Administration, Huaqiao University, Quanzhou 362021, China
Zhengwei Wang: College of Business Administration, Huaqiao University, Quanzhou 362021, China
Xiaoji Wan: College of Business Administration, Huaqiao University, Quanzhou 362021, China
Hailin Li: College of Business Administration, Huaqiao University, Quanzhou 362021, China

Mathematics, 2022, vol. 10, issue 16, 1-17

Abstract: Traditional topic research divides similar topics into the same cluster according to clustering or classification from the perspective of users, which ignores the deep relationship within and between topics. In this paper, topic analysis is achieved from the perspective of the topic network. Based on the initial core topics obtained by the keyword importance and affinity propagation clustering, co-occurrence time series between topics are constructed according to time sequence and topic frequency. Subsequence segments of each topic co-occurrence time series are divided by sliding windows, and the similarity between subsequence segments is calculated. Based on the topic similarity matrix, the topic network is constructed. The topic network is divided according to the community detection algorithm, which realizes the topic re-clustering and reveals the deep relationship between topics in fine-grained. The results show there is no relationship between topic center representation and keyword popularity, and topics with a wide range of concepts are more likely to become topic network centers. The proposed approach takes into account the influence of time factors on topic analysis, which not only expands the analysis in the field of topic research but also improves the quality of topic research.

Keywords: topic network; co-occurrence time series; sliding window; community detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/16/2846/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/16/2846/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:16:p:2846-:d:884949

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().