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All You Need Is Artificial Intelligence to Recognize Your Customer Haters Towards Your Brand
Abstract
This study explores how artificial intelligence can enable practitioners to identify and mitigate consumer brand hate—a complex negative emotion encompassing anger, contempt, disgust, and fear. As one of the most damaging consumer sentiments, brand hate threatens brand equity and customer relationships; yet distinguishing genuine haters from transient complainers remains challenging. The authors propose a multimodal AI framework that synergistically analyzes four behavioral dimensions: (1) natural language processing (NLP) of speech content for hostile lexicon and semantic patterns, (2) computer vision-based facial expression analysis targeting contempt and disgust micro-expressions, (3) paralinguistic voice analysis detecting anger-prosody and vocal tension, and (4) kinematic body language interpretation of defensive/aggressive gestures. The integrated approach demonstrates superior accuracy over unimodal methods by capturing the interplay between explicit verbal hate and implicit nonverbal cues.
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