Sentiment Classification and the Relationship of Textual Characteristics to Tweet Engagement Levels on Shopee Services
Klasifikasi Sentimen Dan Hubungan Karakteristik Teks Terhadap Tingkat Engagement Tweet Pada Layanan Shopee
DOI:
https://doi.org/10.21070/ups.9982Keywords:
Analysis Sentiment, Engagement, Naive Bayes, SVM, FP-Growth, TwitterAbstract
Social media Twitter (X) is widely used by users to express opinions, complaints, and experiences related to e-commerce services such as Shopee. The high volume of conversations creates the need for automated analysis to understand user sentiment and engagement behavior. This study focuses on classifying sentiment in Shopee-related tweets and examining the relationship between textual characteristics and engagement levels. Sentiment classification models were developed using Naïve Bayes and Support Vector Machine (SVM) algorithms with an 80:20 train–test data split. Engagement analysis was supported by FP-Growth, WordCloud, and Crosstab techniques. The results show that both models achieved an accuracy of 68%, with SVM demonstrating more balanced performance across sentiment classes. FP-Growth analysis revealed dominant patterns in positive sentiment with low engagement, while high engagement showed no strong association patterns. These findings suggest that engagement is influenced not only by sentiment, but also by external factors and account characteristics.
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