Â¬Â¬IMPLEMENTASI NAÃVE BAYES DAN SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN ULASAN PADA GOOGLE PLAY - Yoke Lucia Renica Rehatalanit; Marcelino Paul Edwil Longdong; Achmad Ramadhany (Fakultas Ilmu Komputer dan Desain - Unsurya, 2024)
Reviews on Google Play describes user sentiment towards the application according to the ratings and comments are given. In practice, there is often a discrepancy between the rating and the comments given, resulting in a biased sentiment, so it is necessary to analyze the review to find out the sentiment contained therein. In collecting data from the Google Play site using the Web Scraping technique with the google-play-scraper package from Python. Reviews that are successfully scraped then go through the preprocessing stage so that the data set is more structured. In the next stage, the data set is labeled based on the rating, and given a weight using TF-IDF. After classifying using the Naïve Bayes and Support Vector Machine methods, then evaluating using the confusion matrix, and validating using K-Fold Cross Validation. Research results using the Naïve Bayes method and Support Vector Machine for sentiment analysis on the Google Play website, the Naïve Bayes method produces 87.82% accuracy, 58.90% precision, 60.08% recall, while the Support Vector Machine method produces 90% accuracy .01% , precision 61.89%, recall 60.18%.