Discovering Community Interests Approach to Topic Model with Time Factor and Clustering Methods
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Many methods of discovering social networking communities or clustering of features are based on the networkstructure or the content network. This paper proposes a community discovery method based on topic modelsusing a time factor and an unsupervised clustering method. Online community discovery enables organizationsand 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 ecommerceand Internet development. The objective of this work is to find clusters (communities) such that eachcluster’s nodes contain topics and individuals having similarities in the attribute space. In terms of social mediaanalytics, the method seeks communities whose members have similar features. The method is experimentedwith and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommercesites in various sectors from 2016 to 2019. The experimental results demonstrate the effectivenessof the proposed method over other methods.
Abstract
Many methods of discovering social networking communities or clustering of features are based on the networkstructure or the content network. This paper proposes a community discovery method based on topic modelsusing a time factor and an unsupervised clustering method. Online community discovery enables organizationsand 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 ecommerceand Internet development. The objective of this work is to find clusters (communities) such that eachcluster’s nodes contain topics and individuals having similarities in the attribute space. In terms of social mediaanalytics, the method seeks communities whose members have similar features. The method is experimentedwith and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommercesites in various sectors from 2016 to 2019. The experimental results demonstrate the effectivenessof the proposed method over other methods.
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