From Tweets to Telecom Intelligence: Text Mining and Sentiment Analysis of Pakistan’s Cellular Network Competition

Abstract

Social media platforms have become important sources of publicly available user-generated content that can be utilized by organizations to understand customer perceptions, monitor competitors, and support data-driven business decision-making. In highly competitive industries such as telecommunications, social media interactions provide valuable insights into customer engagement, brand visibility, service perception, and market response. This study presents a case study of competitive intelligence based on text mining and sentiment analysis using Twitter/X content from four major Pakistani cellular network companies, namely Mobilink/Jazz, Ufone, Zong, and Telenor. The study examines social media activity patterns, tweet distribution, customer interaction, and sentiment polarity to identify differences in the online presence and engagement strategies of these companies. WordStat was used to support text mining analysis, while SentiStrength was applied to classify tweet sentiment into positive, negative, and neutral categories. The findings indicate that the volume and type of tweets are closely related to customer engagement and brand interaction. Among the analyzed companies, differences were observed in tweet frequency, polarity distribution, and the level of customer response. The results show that Twitter/X can serve as a useful platform for extracting competitive intelligence, especially for understanding public sentiment and improving customer relationship strategies. This study provides practical insights for telecom companies seeking to strengthen their social media-based competitive intelligence models and enhance their responsiveness to customer needs.