Klasifikasi Sentimen Ulasan Buku Pada Platform Shopee: Studi Komparatif Naive Bayes dan SVM Dengan Pembobotan TF-IDF pada Dataset Seimbang
Abstrak
The surge in informal and unstructured product review data in e-commerce has become a major obstacle in manual business evaluation. This study aims to conduct a comparative study of book product review sentiment classification on the Shopee platform using the Multinomial Naive Bayes algorithm and Support Vector Machine (SVM) with a linear kernel. The dataset used consists of 1,101 reviews obtained through web scraping techniques. The sentiment class labeling procedure was carried out manually through cross-validation by three annotators based on Shopee star rating curation and textual content analysis to ensure data validity. After going through the text pre-processing (Sastrawi) stage and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction, the model was tested and its strength was evaluated using the Stratified 10-Fold Cross Validation technique. The test results showed that the SVM algorithm produced slightly superior performance with an accuracy rate of 86.47%, precision of 88.37%, and recall of 84.04%. Meanwhile, Multinomial Naive Bayes achieved 86.29% accuracy, 87.16% precision, and 85.12% recall. In conclusion, although both models demonstrated similar robustness on a balanced dataset, SVM offered a higher level of precision in addressing the specific characteristics of the book review domain, which is characterized by lengthy and informal descriptions in the digital ecosystem.
Identifier
- DOI: https://doi.org/10.24252/shift.v6i2.283
- OpenAlex: https://openalex.org/W7167357104