Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes
Abstract: 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.
Keywords: Indonesian
telecommunication service provider, Multinomial Naïve Bayes, sentiment analysis,
service performance, Twitter
Author: Aisah Rini Susanti,
Taufik Djatna, Wisnu Ananta Kusuma
Journal Code: jptkomputergg170132
