Classification of Sentiment in User Reviews of the Shopee App on Google Play Store Using the Naive Bayes Method
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Abstract
There is currently no relevant and systematic method that can classify reviews into positive and negative groups on the Shopee online shopping application. This study aims to classify reviews in order to provide solutions for the Shopee application in improving its services and customer satisfaction. This analysis is a sentiment classification relying on the Naive Bayes method, consisting of 1,000 reviews from Google Playstore. After training and testing the data with an 80% training data and 20% test data split, the model produced an accuracy value of 0.85, precision of 0.79, recall of 0.95, and an F1-score of 0.86. These values indicate that the model is capable of classifying review sentiment with a high degree of accuracy and a good balance between precision and recall. The model proved to be superior in recognizing reviews with negative sentiment compared to positive sentiment, where the recall for the negative class reached 0.96, while for the positive class it was 0.75. This study provides insight into user perceptions of Shopee and helps the application improve the user experience on the platform.
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References
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