Brand Hate Detector: An R Shiny Application for Automated Detection and Multilevel Classification of Brand Hate in Consumer Reviews

The Brand Hate Detector is an open-source R Shiny application designed to identify, classify, and visualize brand hate in online consumer reviews. Unlike traditional sentiment analysis tools that reduce feedback to simple positive–negative polarity, the Brand Hate Detector employs a hybrid lexicon-based approach that integrates sentiment polarity, emotion profiling using the NRC Emotion Lexicon, and rule-based classification to capture multiple hate intensity levels. The workflow includes automated scraping of brand-specific reviews from ConsumerAffairs.com, negative sentiment filtering, stopword removal, emotion analysis, and a two-stage classification process that assigns reviews to Mild, Moderate, Strong, or Hybrid hate categories. Results are presented through interactive visualizations, including category distributions, reason-specific breakdowns, emotion word clouds, and sentiment–intensity bubble plots. Designed for transparency, reproducibility, and accessibility, the tool bridges academic theory and managerial practice by providing actionable insights into the drivers and emotional dynamics of consumer hostility toward brands. Its modular architecture also allows future extensions to other review platforms, additional languages, and machine learning–based classification methods.