Sentiment Analysis on TikTok Using RapidMiner

Abstract

Users commonly provide feedback on certain applications. Users can provide either positive, negative or neutral reviews. To determine whether the reviews are positive, negative or neutral, this study use sentiment analysis through various methods of text mining and materials. In this study, a sentiment analysis application for TikTok analysis was conducted using RapidMiner.
This project is conducted based on three issues from TikTok which are account review, sound review and video review. These issues are analyzed using Decision Tree, Naive Bayes and k-NN. RapidMiner is used throughout the process to ensure that the data is accurately performed. Then, the result is gathered by checking the accuracy of data based on the three methods. To analyze the data and obtain an exact performance of the outcome, the process of visualization and modelling is required. The analysis of the reviews from the users shows that majority reviews were positive compared to the negative and neutral reviews especially on video issue.