Advanced Sentiment Analysis Models for Crisis-Time Brand Trust Monitoring and Recovery

In an era where brand perception can be reshaped within minutes on digital platforms, organizations face immense reputational risks during crises. Whether due to product failures, executive scandals, data breaches, or socio-political missteps, brand crises elicit public backlash that unfolds rapidly across social media, news outlets, and consumer forums. Monitoring and restoring brand trust under such volatile conditions demands tools capable of real-time, context-sensitive, and nuanced analysis of public sentiment. This paper explores the role of advanced sentiment analysis models in crisis-time brand trust monitoring and recovery. Drawing on recent advancements in natural language processing (NLP), deep learning, and emotion-aware AI, we examine how modern models—such as transformer-based architectures (e.g., BERT, RoBERTa), hybrid rule-based-deep learning systems, and affective computing algorithms—outperform traditional lexicon and statistical techniques in capturing the subtleties of sentiment, emotion, and trust dynamics during brand crises. We develop a conceptual framework that integrates sentiment analytics with crisis communication strategies and outline its application through cross-sector scenarios. Through a systematic literature review, we highlight challenges in multilingual processing, sarcasm detection, temporal sentiment tracking, and model explainability. The paper concludes with recommendations for deploying sentiment analysis tools responsibly, ensuring ethical AI governance, and aligning model outputs with actionable recovery strategies for brand managers.