Sentiment Analysis of Application Reviews using the K-Nearest Neighbors (KNN) Algorithm - Damar Wijati; Prima Dina Atika; Siti Setiawati; Rasim Rasim (PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic, 2024)
Product reviews play a crucial role in evaluating user satisfaction and overall performance. Vidio, one of the over-the-top (OTT) media platforms, offers a wide range of entertainment content, including movies, TV shows, sports events, music shows, lifestyle programs, and more, accessible through its application. Users have the opportunity to provide reviews and feedback on their experience with the Vidio application. Therefore, this research was conducted to analyze user sentiment towards the Vidio application on the Google Play Store platform using the K-Nearest Neighbors (KNN) method. Data for sentiment analysis were randomly selected from the Vidio application based on the most relevant reviews. A total of 3,000 data were analyzed, with 2,238 data in the negative class, 508 data in the neutral class, and 254 data in the positive class. This research used the K-Nearest Neighbors (KNN) method for classifying reviews based on negative, neutral, and positive classes, and the Multiclass Confusion Matrix for model evaluation. With a data split of 70% for training data 30% for testing data, and several n_neighbors of 10 data, the results in an accuracy of 81.6%, precision of 79%, recall of 81.6%, and F1-Score of 77%.