Analisis Sentimen Terhadap Rating dan Ulasan Film dengan menggunakan Metode Klasifikasi Naive Bayes dengan Fitur Lexicon-Based - Nadhif Sanggara Fathullah; Yuita Arum Sari; Putra Pandu Adikara (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2020)
Film is a work of art which liked a lot by movie fans today. The types of films are very diverse and each type of film has its own fans. Every fan has their own assessment of the film they like. Rating becomes an assessment of a film with a certain scale. In addition, the review becomes a translation of fan ratings of the film. The assessment aspects contained in the review include the delivery of stories, shooting techniques, actors, visual effects, etc. In the review itself there are criticisms or comments that contain sentiment towards the film. Sentiment analysis can help film fans determine whether a film has positive or negative sentiments. In order to get the sentiment analysis result, the Naive Bayes Classification Method is used with Lexicon-Based features selection. In the classification process, the appearance of sentiment words are calculated as well as the rating features to determine the sentiment class. Based on the test results, the value of accuracy, precision, and recall has a result of 0.9, 0.9, and 0.9 respectively by selecting the feature in the form of deletion of stopword while the value of accuracy, precision, and recall has a result of 1, 1, and 1 respectively by selecting features in the form of Lexicon-Based.