<![CDATA[Tokopedia, a prominent e-commerce platform in Indonesia, generates vast amounts of user feedback, offering valuable insights into customer satisfaction through sentiment analysis. However, sentiment analysis of app reviews specifically on Tokopedia reviews remains underexplored. Sentiment analysis, also known as opinion mining, categorizes user sentiments into positive or negative, offering insights into user preferences and service shortcomings. While traditional text representation techniques like TF-IDF are widely used for sentiment analysis, they lack the semantic richness provided by advanced word embeddings such as Word2Vec and FastText, which excel at capturing contextual relationships between words. These methods have shown potential to enhance the performance of machine learning models in sentiment analysis tasks. This study evaluates the performance of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naïve Bayes (NB)—combined with Word2Vec and FastText feature extraction. A dataset of 59,811 Tokopedia app reviews was scraped from the Google Play Store, preprocessed, and subjected to feature extraction using Word2Vec and FastText. SVM achieved the best performance, with an accuracy of 89.06% using FastText and 89.02% using Word2Vec. RF ranked second with accuracies of 88.07% for FastText and 88.14% for Word2Vec. NB showed the lowest performance, achieving 84.26% with Word2Vec and 83.73% with FastText. Differences in performance between Word2Vec and FastText embeddings were minimal across all algorithms, highlighting their comparable effectiveness. These results underscore SVM’s consistent superiority across various configurations for sentiment analysis.]]>
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