Penerapan Bernoulli Naïve Bayes untuk Analisis Sentimen Pengguna Twitter terhadap Layanan Online Food Delivery di Indonesia
Keywords:Bernoulli Naïve Bayes, information gain, online food delivery, senntiment analysis, Twitter
Online food delivery is one of the drivers of the digital economy that all societies today are interested in. The trend of these services has intensified as changes in people's behavior and lifestyle in the Covid-19 pandemic. The digital platforms of food delivery services in Indonesia are GoFood, ShopeeFood, and GrabFood, present ease in both competitive transactions and multiple options by consumers. Its widespread use of these platforms certainly generates a variety of reviews and public opinion; one is through tweets on Twitter. This study aims to classify the sentiments on the various reviews into the label of positive and negative sentiments using the Bernoulli Naïve Bayes algorithm. The majority of reviews from March 15, 2022 to March 30, 2022 were positive sentiments, which indicated that people gave a positive impression during these online food delivery service. The results of this study show that Bernoulli Naïve Bayes with the feature selection of information gain generates a good performance in classifying sentiment labels based on accuracy scores obtained at 89%, 87%, 86%, and 85% in all data and each online food delivery platform (GoFood, ShopeeFood, and GrabFood).