Analisis Sentimen Ulasan Pengguna Tiktok pada Google Play Store Berbasis TF-IDF dan Support Vector Machine

Abstract

The rapid development of information and communication technology, particularly in social media platforms, has created an environment where users actively share their experiences and opinions related to various services and applications. One platform that has gained significant popularity is TikTok, a video-sharing application that has become a global phenomenon. With the increasing number of TikTok users, user reviews on distribution platforms such as the Google Play Store have become a crucial source of information. Sentiment analysis of these reviews can provide deep insights into how users respond to the application, while also offering valuable feedback for developers. The research aims to conduct sentiment analysis of TikTok user reviews on the Google Play Store using the Term Frequency-Inverse Document Frequency (TF-IDF) weighting method and the Support Vector Machine (SVM) algorithm as a classification method to achieve optimal results. There are three main stages: the initial stage involves data collection and data pre-processing, followed by the pattern recognition stage, which includes TF-IDF weighting and SVM classification. The final stage consists of evaluation and analysis. The opinion classification obtained includes three categories: positive, negative, and neutral. Based on the evaluation results, the proposed method successfully achieved high accuracy for the 70-30% training-testing split, reaching 84%. The conclusion drawn from these evaluation results indicates that the proposed method can be utilized in the sentiment analysis process of TikTok user reviews.