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p-Index From 2020 - 2025
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This Author published in this journals
All Journal Journal of Computer System and Informatics (JoSYC)
Rifki Alfian Abdi Malik
Telkom University, Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Multi-aspect Sentiment Analysis of Tiktok Application Usage Using FasText Feature Expansion and CNN Method Rifki Alfian Abdi Malik; Yuliant Sibaroni
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2033

Abstract

Among the many social media platforms that have emerged, TikTok is a platform that has the most significant number of subscribers compared to other platforms. However, not all reviews given by TikTok users are good reviews and reviews are often found with slang and not all reviews have real meaning, therefore sentiment analysis is needed for these problems. These reviews will later be analyzed for sentiment according to predetermined aspects, namely feature aspects, business aspects, and content aspects based on reviews written on the Google Play Store, using data crawling techniques and will pass the preprocessing and weighting stages. The weighting method used is Term Frequency-Inverse Document Frequency (TF-IDF). Then, the sentiment analysis process will use the Convolutional Neural Network (CNN) method, and feature expansion will be carried out to determine what words are interrelated with certain words. The purpose of this research is to analyze sentiment using Convolutional Neural Network and fastText feature expansion. The highest accuracy result is 87.74%.
Co-Authors Yuliant Sibaroni