SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA FOR BRAND REPUTATION MANAGEMENT: A REVIEW AND COMPARATIVE ANALYSIS
Abstract
Brand reputation is no longer shaped solely by news reports or periodic customer surveys; instead, it evolves continuously through millions of social media interactions generated every day. This paper presents a comprehensive review of sentiment analysis techniques for brand reputation management, tracing the evolution of methodologies from traditional lexicon-based approaches and early machine learning algorithms to modern transformer-based models such as BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa (Robustly Optimized BERT Pretraining Approach). The study evaluates these approaches using evidence from more than 25 scholarly publications published between 2020 and 2026.
The research compares various sentiment analysis models based on performance metrics such as accuracy and F1-score while examining their practical applications in brand monitoring. It explores important challenges including crisis detection, aspect-based sentiment analysis, multilingual content, sarcasm detection, and the dynamic nature of internet slang. The paper also discusses how platform-specific characteristics influence sentiment analysis outcomes and brand perception across digital channels.
The findings indicate that transformer-based models, particularly BERT and its variants, consistently outperform conventional machine learning techniques in sentiment classification tasks. However, their high computational requirements present challenges for real-time deployment in large-scale brand monitoring systems. The study concludes by identifying future research opportunities in multimodal sentiment analysis, privacy-preserving federated learning, and more efficient AI models for real-time reputation management.
Keywords
Aspect-Based Sentiment Analysis, BERT, Brand Reputation Management, Natural Language Processing, Online Reputation, RoBERTa, Social Media Analytics, Transformer Models
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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