Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dan Lexicon Based Features - Arifin Kurniawan; Indriati Indriati; Sigit Adinugroho (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
The rapid development of information technology has resulted in many people writing their opinions on social media as in the KASKUS forum. KASKUS is an online forum site that provides a place to find information and share hobbies. One is called Movies forum which contains discussions about a movie that has been watched. Users writes their opinion about a film whether the film is good or bad. These opinions can be analyzed to determine how the user feedback about the film in order to produce useful output for the filmmaker by perform sentiment analysis to classify opinions into positive or negative classes. The process of sentiment analysis was performed using methods Naive Bayes for classification and Lexicon Based Features to weight the sentiment value of a word. The process starts from text preprocessing, term weighting, Naive Bayes training, and Naive Bayes testing with Lexicon Based Features weighting using Barasa's lexicon. Based on the results of tests performed, using Naive Bayes and Lexicon Features Based method, the values of accuracy, precision, recall, and f-measure were 0.8, 0.8, 0.8 and 0.8. While using the Naive Bayes method without Lexicon Based Features, the values of accuracy, precision, recall, and f-measure are 0.95, 1, 0.9 and 0.9474. So, the use of Naive Bayes and Lexicon Based Features methods still cannot provide better results.