Leveraging BERT and Sentiment Analysis Algorithms for Enhanced AI-Driven Brand Sentiment Monitoring

Abstract

This research paper explores the integration of BERT (Bidirectional Encoder Representations from Transformers) with advanced sentiment analysis algorithms to enhance the performance of AI-driven brand sentiment monitoring systems. As companies increasingly rely on real-time sentiment tracking to guide strategic decisions, the need for precise and context-aware analysis becomes vital. Traditional sentiment analysis tools often struggle with context comprehension, leading to misinterpretations, especially in nuanced or idiomatic language. BERT, with its deep learning architecture, excels in understanding context and semantic relationships, offering a robust solution to this challenge. This study systematically examines the effectiveness of BERT in processing social media data, product reviews, and online content, comparing its performance against conventional sentiment analysis methodologies like LSTM (Long Short-Term Memory) and traditional lexicon-based approaches. The paper presents a comprehensive framework that combines BERT's superior language modeling capabilities with sentiment-specific fine-tuning strategies, achieving higher accuracy in sentiment classification tasks. Experimental results demonstrate significant improvements in sentiment detection precision and recall, showcasing BERT's ability to identify subtle sentiment shifts and complex linguistic patterns. Furthermore, the research addresses practical implications, discussing scalable deployment of the proposed system for real-time brand monitoring and highlighting the potential reduction in reputational risks through proactive sentiment management. The findings suggest that leveraging BERT within sentiment analysis frameworks can substantially elevate the effectiveness of AI-driven tools in capturing brand sentiment dynamics, offering an innovative approach to sentiment monitoring in today's fast-paced digital landscape.