Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes

Telecommunication users are rapidly growing each year. As people keep demanding a better service level of Short Message Service (SMS), telephone or data use, service providers compete to attract their customer, while customer feedbacks in some platforms, for example Twitter, are their souce of information. Multinomial Naive Bayes Tree, adapted from the method of Multinomial Naive Bayes and Decision Tree, is one technique in data mining used to classify the raw data or feedback from customers.Multinomial Naive Bayes method used specifically addressing frequency in the text of the sentence or document. Documents used in this study are comments of Twitter users on the GSM telecommunications provider in Indonesia.This research employed Multinomial Naive Bayes Tree classification technique to categorize customers sentiment opinion towards telecommunication providers in Indonesia. Sentiment analysis only included the class of positive, negative and neutral. This research generated a Decision Tree roots in the feature "aktif" in which the probability of the feature "aktif" was from positive class in Multinomial Naive Bayes method. The evaluation showed that the highest accuracy of classification using Multinomial Naive Bayes Tree (MNBTree) method was 16.26% using 145 features. Moreover, the Multinomial Naive Bayes (MNB) yielded the highest accuracy of 73,15% by using all dataset of 1665 features. The expected benefits in this research are that the Indonesian telecommunications provider can evaluate the performance and services to reach customer satisfaction of various needs.