Clustering student Instagram accounts using author-topic model Online publication date: Tue, 06-Jul-2021
by Nur Aini Rakhmawati; N.F. Faiz; Irmasari Hafidz; Indra Raditya; Pande Dinatha; Andrianto Suwignyo
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 19, No. 1, 2021
Abstract: This study proposes topic model to cluster a group of high school teenager's Instagram account in Surabaya, Indonesia by using the author-topic models method. We collect valid 235 Instagram accounts (133 female, 102 male students). We gather a total of 3,346 captions of Instagram posts from 18 senior high schools. We find topics that define their Instagram's post or caption; these seven topics are namely: feeling, Surabaya events, photography, artists, vacation, religion and music. Through the process, the lowest perplexity come from 90 iterations, which suggests six groups of topics. The six topics are concluded based on the lowest perplexity value and labelled according to the words included in the topic. The topic of photography discussed by six schools. Photography, artists and vacation are discussed by three schools, while feeling and religion and music are being discussed by two and one school respectively.
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