Comparison of Support Vector Machine and Naïve Bayes Algorithms in Sentiment Analysis of Tiktokshop Application User Reviews

This study presents a comparative analysis of Support Vector Machine (SVM) and Naïve Bayes algorithms for sentiment analysis of TikTokShop application user reviews. As TikTokShop emerges as an innovative platform integrating social media with e-commerce, understanding user sentiments becomes crucial for both consumers and businesses. A balanced dataset of 3,000 user reviews (1,000 positive, 1,000 neutral, and 1,000 negative) was collected through web scraping from Google Play Store. Following comprehensive preprocessing including cleansing, case folding, normalization, tokenization, stopword removal, and stemming, the data was vectorized using TF-IDF. Performance evaluation utilized accuracy, precision, recall, F1-score, confusion matrix, and 10-fold cross-validation. Results demonstrate that SVM consistently outperformed Naïve Bayes with higher accuracy (68.86% vs. 64.48%), precision (68.43% vs. 64.19%), and F1-score (68.58% vs. 62.46%). SVM exhibited balanced classification across all sentiment categories, while Naïve Bayes excelled at identifying negative sentiments (94.1% accuracy) but struggled significantly with neutral reviews (38.5%). Despite SVM's superior performance, Naïve Bayes demonstrated remarkable computational efficiency, with training time 224 times faster than SVM. The study reveals complementary strengths between the algorithms, suggesting potential value in ensemble approaches. These findings contribute to the understanding of sentiment analysis in video-based e-commerce platforms and provide valuable insights for businesses seeking to leverage user feedback for improved decision-making.