Hybrid Naive Bayes TF-IDF Algorithm and Lexicon Approach for Sentiment Analysis of Reviews

Ahmad Harits Ramadhani, Huzaifah Qahar Djauhari, Vincent Lius, Andi Nugroho


Amidst the increasing reliance on social media for public expression, accurate sentiment analysis has become essential, notably in assessing application reviews. This study focuses on the need for precise sentiment classification, exemplified by the prevalence of negative feedback in certain app reviews. To address this, we propose a hybrid approach integrating the Naive Bayes algorithm with lexicon-based sentiment labeling and TF-IDF for the model training. Using a dataset of 5000 reviews, we explore Indonesian Lexicons, specifically InSet and SentiStrengthID, to label sentiments. Our primary objective is to classify reviews into positive and negative sentiments, providing valuable insights. Through evaluating the effectiveness of combining Naive Bayes with TF-IDF and lexicon-based methods, this study contributes to a deeper understanding of sentiment analysis in the context of application reviews.

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