MABBI – Research conducted by Aisah Rini Susanti, Taufik Djatna, and Wisnu Ananta Kusuma from IPB University and Djuanda University entitled 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 customers, while customer feedbacks in some platforms, for example Twitter, are their source of information. Multinomial Naïve Bayes Tree, adapted from the method of Multinomial Naïve Bayes and Decision Tree, is a technique in data mining used to classify the raw data or feedback from customers. Multinomial Naïve Bayes method used specifically addressing frequency in the text of the sentence or document. Documents used in this study are comments from Twitter users on the GSM telecommunications provider in Indonesia. This research employed Multinomial Naïve Bayes Tree classification technique to categorize customers sentiment opinion towards telecommunication providers in Indonesia. Sentiment analysis only included the classes of positive, negative and neutral. This research generated a Decision Tree roots in the feature “active” in which the probability of the feature “active” was from positive class in Multinomial Naive Bayes method. The evaluation showed that the highest accuracy of classification using Multinomial Naïve Bayes Tree (MNBTree) method was 16.26% using 145 features. Moreover, the Multinomial Naïve 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 achieve customer satisfaction of various needs. (Tri/MABBI)
Read more: http://telkomnika.uad.ac.id/index.php/TELKOMNIKA/article/view/4284
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