Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering

Mingyang Li, Xinhua Bi, Limin Wang, Xuming Han, Lin Wang, Wei Zhou

Source Title: Journal of Organizational and End User Computing (JOEUC)34(2)

ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799893257|DOI: 10.4018/JOEUC.302893

MLA

Li, Mingyang, et al. "Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering." JOEUC vol.34, no.2 2022: pp.1-25. http://doi.org/10.4018/JOEUC.302893

APA

Li, M., Bi, X., Wang, L., Han, X., Wang, L., & Zhou, W. (2022). Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering. Journal of Organizational and End User Computing (JOEUC), 34(2), 1-25. http://doi.org/10.4018/JOEUC.302893

Chicago

Li, Mingyang, et al. "Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering," Journal of Organizational and End User Computing (JOEUC) 34, no.2: 1-25. http://doi.org/10.4018/JOEUC.302893

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Abstract

Text similarity measurement is a link between basic research such as text modeling and upper-level application research of text potential information. In order to improve the accuracy of text similarity measurement, this paper proposes a semantic similarity calculation method integrating word2vec model and TF-IDF, and applies it to the density peak clustering of Chinese text data consulted by patients in online medical community. Experimental results show that the proposed similarity measurement method is superior to the traditional method. Furthermore, the study is among the first to apply the density peak clustering algorithm to online medical community, which offers a reference for how to find out user demands from medical text data in the big data environment.