Twitter Sentiment to Analyze Net Brand Reputation of Mobile Phone Providers

Nur Azizah Vidya, Mohamad Ivan Fanany, Indra Budi

Research output: Contribution to journalConference articlepeer-review

62 Citations (Scopus)


We may see competition among mobile providers to acquire new customers through campaign and advertisement war, especially on social media. The problem arises on how to measure the brand reputation of these providers based on people response on their services quality. This paper addresses this issue by measuring brand reputation based on customer satisfaction through customer's sentiment analysis from Twitter data. Sample model is built and extracted from 10.000 raw Twitter messages data from January to March 2015 of top three mobile providers in Indonesia. We compared several features extractions, algorithms, and the classification schemes. After data cleaning and data balancing, the sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. We measure customer satisfaction on five products: 3G, 4G, Short Messaging, Voice and Internet services. This paper also discusses some correlated business insights in a telecommunication services industry. Based on the overall comparison of these five products, the NBR scores for PT XL Axiata Tbk, PT Telkomsel Tbk, and PT Indosat Tbk are 32.3%, 19.0%, and 10.9% respectively.

Original languageEnglish
Pages (from-to)519-526
Number of pages8
JournalProcedia Computer Science
Publication statusPublished - 2015
Event3rd Information Systems International Conference, 2015 - Shenzhen, China
Duration: 16 Apr 201518 Apr 2015


  • Indonesia
  • Naïve Bayes
  • brand reputation
  • decision tree
  • sentiment analysis
  • support vector machine
  • twitter


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