Customer Sentiment Analysis Toward MSME Products On Tiktok Social Media Using Naive Bayes Algorithm

Abstract

This The rapid growth of TikTok social media has made it an effective digital promotion medium for Micro, Small, and Medium Enterprises (MSME). —The large number of comments left by users serves as a valuable source of information to measure customer perception and satisfaction. However, the characteristics of social media text, which are unstructured, informal, and contain slang and emojis, make manual analysis difficult. This study aims to classify customer review sentiments regarding MSME food products on TikTok into positive, negative, and neutral categories using the Multinomial Naive Bayes algorithm. The dataset consists of 1,041 clean comment entries collected via web scraping. Text preprocessing steps include case folding, slang normalization, stopword removal, and the removal of empty and duplicate data. Sentiment labeling was conducted using a lexicon-based approach, while feature weighting was performed using Term Frequency-Inverse Document Frequency (TF-IDF). The dataset was split into an 80% training set (832 data points) and a 20% testing set (209 data points). The model performance evaluation showed that the Naive Bayes algorithm achieved an overall accuracy rate of 75.60%. The model demonstrated high performance on the neutral class with an F1-score of 0.85 and a precision of 1.00 for the positive class. This research provides a practical contribution for MSME owners in evaluating customer feedback and developing more responsive digital marketing strategies‖.