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

Abstract

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 Naï ve 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-Naï ve bayesian, sentiment analysis, telephone provider, text mining.

Sentiment Analysis About Indonesian Lawyers Club Television Program Using K-Nearest Neighbor, Naïve Bayes Classifier, And Decision Tree

“…Sentiment analysis can also be interpreted as learning an opinion, problem, feeling, or emotion from someone or the public in responding to something in the form of text or writing. In determining a sentiment, it is done by calculating some of the words contained in sentences, documents, or text [6]. Rapidminer is software that can be used to process data mining.…”