The Use of Naïve Bayes Algorithm in Sentiment Analysis of Grab Application Reviews - Febri Aditiya; Donny Maulana; Edora (Penerbit Jurnal Indonesia, 2024)
This study aims to analyze the sentiment of Grab application user reviews using the Naïve Bayes algorithm. In conducting the analysis, how well the Naïve Bayes algorithm can analyze Grab application user review data. Thus, this process refers to the elements that affect the accuracy of the Naïve Bayes analysis. to analyze the sentiment of Grab application user reviews using the Naïve Bayes algorithm. Thus, it not only evaluates the accuracy of the Naïve Bayes method, but also considers the problem of imbalanced class, because in reality not all datasets can be accessed perfectly. To consider the problem of data imbalance, this study requires the Adaptive Synthetic Sampling or ADASYN technique which is used in machine learning to overcome the problem of class imbalance in the dataset. The tools used in processing the algorithm in the method and conducting the analysis use Google Colab. This study focuses on the classification of positive and negative sentiments from user reviews taken by the scraping process from the Google Play Store platform. The analysis process involves data preprocessing, including tokenization, stemming, and word weighting, to improve the accuracy of sentiment classification. Based on the results of this study, the Naïve Bayes model sought an accuracy result of 85.33%, the precision result obtained was 80.55% and the recall result in this research test was 79.09%, from these results by implementing calculations using a confusion matrix and dividing the data into testing data and training data