Topic Modelling Using Non-Negative Matrix Factorization (NMF) for Telkom University Entry Selection from Instagram Comments
نویسندگان
چکیده
The development of information technology is increasingly rapid, such as social media, which has much influence. Social media a place or used to express and various opinions on topic. One example Instagram. Instagram platform with many features, posting photos, videos, comments, likes, others. comments feature that contained public opinion can be data. Nothing but the post SMB Telkom University account about entrance university. In posts university, users comment post. This convenient for marketing team get topics discussions most followers need from University's account. Therefore, topic modelling users' perceptions posted university was carried out using Nonnegative Matrix Factorization (NMF) method. After doing several research scenarios, best coherent value obtained 0.60628 4 topics.
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ژورنال
عنوان ژورنال: Journal of Computer System and Informatics
سال: 2022
ISSN: ['2714-8912', '2714-7150']
DOI: https://doi.org/10.47065/josyc.v3i4.2212