Sentiment Analysis and Natural Language Processing for Consumer Insight: A Cross-Industry Framework for Retail Decision Support

Abstract

This review investigates how sentiment analysis and natural language processing can transform consumer-generated text into reliable intelligence for retail decision-making across industries. The study aims to synthesise conceptual foundations, data ecosystems, analytical techniques, sectoral applications, implementation challenges, and emerging technological opportunities, while developing an integrated framework for managerial use. A structured review approach was adopted, drawing on interdisciplinary scholarship from marketing, information systems, artificial intelligence, consumer behaviour, operations, and decision science. The analysis considered online reviews, social media content, surveys, service transcripts, chatbot interactions, and complaint records as major sources of opinion-bearing data. The findings indicate that sentiment intelligence is most valuable when it moves beyond simple polarity classification to identify emotions, intentions, topics, and specific experience attributes. Lexicon-based methods, machine-learning classifiers, deep neural networks, transformers, and large language models offer complementary advantages in transparency, scalability, contextual understanding, and predictive performance. Cross-industry evidence further shows that retail, banking, telecommunications, hospitality, healthcare, and digital platforms share common analytical requirements, although sectoral language, cultural variation, regulatory sensitivity, and operational priorities demand local adaptation. Significant barriers include biased datasets, fake reviews, multilingual complexity, privacy exposure, limited explainability, fragmented systems, and weak organisational readiness. The review concludes that sentiment analysis should be embedded within a governed decision-support architecture connecting data acquisition, contextual interpretation, human oversight, managerial action, and continuous feedback. It recommends representative dataset development, privacy-preserving analytics, explainable models, cross-functional governance, and stronger research on African and low-resource languages. These measures are essential for achieving ethical, responsive, and evidence-based consumer insight. Future studies should evaluate causal impacts on retention, service recovery, conversion, and long-term organisational performance.