Analisis Sentimen Ulasan Pengguna Aplikasi Alibaba.Com pada Google Playstore Menggunakan Naïve Bayes - Rahman, Rafi Fadhlur; Irwiensyah, Faldy (Journal of Computer System and Informatics (JoSYC), 2024)
Alibaba.com, as one of the leading platforms, continues to strive to improve its services based on user feedback. One approach used is the collection of user reviews on the Google Play Store. To enhance service quality and user experience, sentiment analysis of these reviews becomes crucial. In this study, the Naive Bayes algorithm is applied to analyze the sentiment of the reviews with the aim of determining whether the sentiment is positive or negative. The data, consisting of reviews, was obtained through web scraping, resulting in 998 reviews that were processed through preprocessing stages. The dataset was then divided into training and testing data with a 60:40 ratio, where 599 reviews were manually labeled for training, and 399 reviews were used as test data. The Naive Bayes algorithm subsequently categorized the reviews as either positive or negative sentiment. An evaluation with a confusion matrix was then used to assess performance, this model showed an accuracy of 77.44%, precision of 83.39%, and recall of 85.16%. A total of 721 reviews were categorized as positive sentiment, while 277 reviews were categorized as negative sentiment. The main issues identified in the negative reviews included challenges related to language and payment. Additionally, there were complaints regarding online buying and selling fraud, which is a significant issue on this platform. Many users reported negative experiences related to transactions that did not match expectations, items that were not received, or products that did not match their descriptions. This highlights the importance of better verification and security systems to protect users from fraud. This study demonstrates that the Naive Bayes algorithm is quite efficient in analyzing user review sentiments on the Alibaba.com application.