Sentiment Analysis of TikTok Studio Application Reviews on Google Play Store
Keywords:
Sentiment Analysis, Logistic Regression, TikTok Studio Reviews, TF-IDF, SMOTEAbstract
Sentiment analysis is a text mining approach used to identify user opinions toward an application, product, or service. User reviews of the TikTok Studio application on the Google Play Store can be utilized to determine the overall sentiment tendencies of users toward the application. This study aims to perform sentiment analysis on TikTok Studio user reviews using Logistic Regression. The research stages include data collection through web scraping, text preprocessing, sentiment labeling using Valence Aware Dictionary for sEntiment Reasoning (VADER), feature representation using Term Frequency–Inverse Document Frequency (TF-IDF), data splitting into training data (80%) and testing data (20%), class balancing using the Synthetic Minority Oversampling Technique (SMOTE), and model training and evaluation using Logistic Regression. The dataset consists of 9,247 user reviews, of which 8,717 valid reviews were obtained after preprocessing. The sentiment labeling process resulted in 6,090 positive reviews, 271 negative reviews, and 2,356 neutral reviews, where neutral reviews were excluded, yielding a final dataset of 6,361 reviews. Based on the evaluation of 1,273 testing data, the Logistic Regression model achieved an accuracy of 97.64%, with precision, recall, and F1-score (macro average) of 0.82, 0.97, and 0.88, respectively.
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