Analisis Sentimen Berbasis Aspek pada Ulasan Pelanggan Restoran Bakso President Malang dengan Metode Naive Bayes Classifier - Whita Parasati; Fitra Abdurrachman Bachtiar; Nanang Yudi Setiawan (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2020)
Bakso President Malang is a restaurant that has been established since 1977. The number of competitors in the same industry makes Bakso President Malang highly appreciate the opinions of customers regarding the products and services they provide to increase customer satisfaction. However, Bakso President Malang does not have customer opinion data, nor does the application of technology in processing and analyzing data that can produce information about customer perspectives on customer satisfaction aspects. One way to get the perspective of customers of Bakso President Malang on aspects of customer satisfaction is through sentiment analysis conducted at the aspect level. The method used for sentiment analysis is classification using the Naive Bayes algorithm. This study uses 2,152 customer review data from 2012 to 2019. Customer review data is obtained through Web Scraping techniques on the TripAdvisor and Google Review sites. Sentiment analysis in each aspect produces an accuracy value of 88% in the Food aspect, 76% in the Service aspect, and 84% in the Atmosphere aspect. The results of the sentiment analysis classification are visualized in the form of a dashboard that is accompanied by a filter based on time, aspects, and sentiments.