NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINES UNTUK SENTIMENT ANALYSIS

Text mining refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divising of patterns and trends through means such as statistical pattern learning. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling. This research discussed the opinions classification as positive opinions and negative opinions on English and Indonesian language data using the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). In this study, both NBC and SVM method gives a good sentiment analysis performance in classifying opinion for English and Indonesia language. The experimental results showed that SVM method gives better performance than NBC method for classifying English opinions. Whether NBC method gives better performance for classifying negative opinions experimental data on Indonesian language.