Brand Hate: Decoding Emotional Trends in the Indian Telecommunication Industry Through Sentiment Analysis

Abstract

The growing number of brand hate cases in the competitive market is gaining the attention of researchers and making a call for studies to explore the concept of brand hate thoroughly. Moreover, the application of cutting-edge technologies in emotion detection is also growing rapidly. This study used the vast capabilities of Python’s rich ecosystem of natural language processing (NLP) libraries and machine learning frameworks in the business domain to investigate a real-world brand hate incident that occurred in the Indian telecommunication industry and explore customers’ opinions and sentiments for telecom brands. It primarily aims to explore the brand hate of customers by using the sentiment analysis approach to identify their hidden emotions. Qualitative data, scraped from Facebook comments posted by the general public, was analyzed to examine the customers’ hidden sentiments. The study’s result shows how an unfavorable action taken by the company caused hate among the customers and influenced them to take action. Customers expressed unfavorable emotions and aggression toward the company using negative words in social media comments that fluctuate over the period. Those who voiced aggression further expressed their intention to switch to another telecom operator. The insights of the study can assist businesses in understanding their customers’ behaviour and aid management in devising actionable strategies to resolve customer hate issues and improve consumer-brand relationships.