Purchase Intention and Sentiment Analysis on Twitter Related to Social Commerce

Abstract

Social commerce is a digital and efficient solution to transform existing commerce and address contemporary issues. TikTok Shop, a popular and trending social commerce platform, competes with established competitors like Facebook Marketplace and Instagram Shop. TikTok Shop offers benefits and incentives to attract users for both sales and product purchases. In this study, various algorithmic approaches such as Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, LGBM Boost, Ada Boost, and Voting Classifier are utilized to analyze and compare sentiments expressed on Twitter regarding Facebook, Instagram, and TikTok. The aim is to determine the methods with the best performance and identify the social commerce platform with the highest purchase intention and positive sentiment. The results indicate that TikTok has more positive sentiment than Facebook and Instagram at 93.07% with the best-performing classification model, Decision Tree. In conclusion, TikTok exhibits the highest positive sentiment percentage, indicating a greater number of positive reviews compared to Facebook and Instagram. According to the theory of evaluation scores for measuring model performance, values above 0.90 represent models with good performance.