Analyzing User Review Sentiments in the Itemku Application Using the Naive Bayes Classifier Algorithm
Abstract
This research will apply sentiment analysis techniques to analyze user reviews for the Itemku platform using the Naïve Bayes Classifer algorithm. The aim is to classify user opinions as positive sentiment or negative sentiment based on their sentiment towards the Itemku platform. Data is collected from user reviews on Google Play Store using the appfollow website. The collected data underwent text preprocessing, including case folding, tokenizing, filtering, stemming, and TF-IDF weighting. This process converts unstructured review data into a structured format. The Naïve Bayes classification algorithm is then used to classify sentiments with 80% of the data used for training and 20% of the data for testing. The results reveal an accuracy rate of 76% in sentiment classification, demonstrating the effectiveness of the Naïve Bayes approach. Wordcloud visualizations are generated to identify keywords that are frequently mentioned in positive and negative sentiment reviews. The results of this study show high user satisfaction with the Itemku platform, as shown by the positive sentiments expressed in user reviews. This research contributes to understanding the opinions of users, provides insights to improve service quality and optimize user experience.
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