Thanh Ho, Tran Duy Thanh, Journal of Information Processing Systems Vol. 17, No. 1, pp. 163-177, Feb. 2021  

https://doi.org/10.3745/JIPS.04.0206
Keywords: Clustering method, Community Interests, Feature Vectors Social Network, Topic Model, Time Factor, User eXperience
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Abstract

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users’ interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster’s nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

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