Aisah Rini Susanti, Aisah Rini
Bogor Agricultural University

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Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes Susanti, Aisah Rini; Djatna, Taufik; Kusuma, Wisnu Ananta
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (422.528 KB) | DOI: 10.12928/telkomnika.v15i3.4284


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 Naïve Bayes Tree, adapted from the method of Multinomial Naïve Bayes and Decision Tree, is one 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 of 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 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 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 reach customer satisfaction of various needs.