Sentiment analysis of e-commerce application in Traveloka data review on Google Play site using Naïve Bayes classifier and association method

Abstract

Abstract E-commerce is a business operation model which rapidly growing today. Many business actors and the customer take advantage of E-commerce itself. Thus, it influences people’s socially and economically. Traveloka is one of the best e-commerce applications that is often visited in Indonesia. Each application allows users to post an application review. The review aims to evaluate and improve the quality of the future product. For that purpose, analysis sentiment can be used to classify the review into positive or negative sentiment. Sentiment analysis can provide information that can be extracted. From the observed data, it can provide useful information for those who need it. Some sentiment analysis stages contain sentiment data collection, data preprocessing, term weighting using TF-IDF, sentiment labeling using sentiment scoring, review data classification using the Naïve Bayes Classifier method, and text association. The model was evaluated using 10 Fold Cross-Validation. Measurements were made with the Confusion Matrix. The results obtained from the reviews given by Traveloka users on Google Play using the Multinomial Naïve Bayes was obtained overall accuracy in 91.20% and kappa accuracy in 59,56 %. The higher overall accuracy value and kappa accuracy obtained, the better performance of the classification model.