Sentiment Analysis of Social Media Data for Brand Reputation Management: A Review and Comparative Analysis

Sentiment Analysis of Social Media Data for Brand Reputation Management: A Review and Comparative Analysis

Abstract

The creation and modification of reputation now occur in real time through millions of social-media posts. This research discusses the development of sentiment-analysis methods, from lexicon-based classifiers and early machine-learning algorithms to transformer-based models such as BERT and RoBERTa. Their results are compared using accuracy, F1-score, applications, and other factors based on at least 25 sources published between 2020 and 2026. The research explores platform characteristics, crisis-detection methods, aspect-oriented analysis, and challenges involving sarcasm, multiple languages, and constantly changing slang. BERT-type systems are found to be the best performers, although their high computing requirements constrain deployment in real-time brand-monitoring systems. Future research directions include multimodal analysis and privacy-preserving federated monitoring.

Authors

Gunjan Saxena, Surjeet Kishor, Sudeep Kumar, Sonali Kumari, Sumit Kumar

Institution

Noida Institute of Engineering & Technology (MCA Institute), Greater Noida, India

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