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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(1): 106-109 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.395

Using Text Mining to Analyze Mobile Phone Provider Service Quality (Case Study: Social Media Twitter)

Calvin and Johan Setiawan
Abstract—Competition between telephone providers to attract new customers can be seen through advertisment war on TVs, posters and radios nearly every moment. Question is arise on how do we measure the quality of these providers in order choose the best one for oneself. This paper is written to solve the question by measuring customers satisfaction by using text mining. Sample model is extracted from social media Twitter and the sentiment polarity is measured using Naïve Bayes classifier method. The model shows a promising result on defining the popularity based on customer's satisfaction and therefore defining the best provider to be used

Index Terms—Naïve bayesian, sentiment analysis, telephone provider, text mining.

Calvin and Johan Setiawan are with the Information System Department, Faculty of Information and Communication Technology, Multimedia Nusantara University, Scientia Boulevard Street, Gading Serpong, Tangerang, Banten-15811, Indonesia (e-mail: calv.axl@gmail.com; johansetiawanumn@gmail.com).


Cite:Calvin and Johan Setiawan, "Using Text Mining to Analyze Mobile Phone Provider Service Quality (Case Study: Social Media Twitter)," International Journal of Machine Learning and Computing vol.4, no. 1, pp. 106-109, 2014.

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