Enhancing Brand Sentiment Monitoring through Hybrid AI Techniques: Leveraging Sentiment Analysis, Natural Language Processing, and Transformer-Based Models

Abstract

This research paper explores the augmentation of brand sentiment monitoring by integrating hybrid artificial intelligence (AI) methodologies, focusing specifically on sentiment analysis, natural language processing (NLP), and advanced transformer-based models. The rapidly increasing volume of online consumer data necessitates more sophisticated tools to accurately gauge sentiment towards brands. Our study proposes a hybrid AI framework that combines traditional sentiment analysis techniques with modern NLP approaches and state-of-the-art transformer architectures like BERT and GPT. We conduct a comprehensive evaluation using a diverse dataset comprised of social media posts, product reviews, and news articles, ensuring the robustness of our model across various contexts and sources. The proposed hybrid model demonstrates a significant improvement in sentiment classification accuracy and context comprehension when compared to existing methods. Additionally, we highlight the ability of transformer-based models to understand nuanced language, slang, and regional vernacular, which are often limitations in conventional sentiment analysis tools. The research identifies key performance metrics, such as precision, recall, and F1-score, and benchmarks them against baseline models to substantiate the enhanced efficacy of the hybrid approach. The findings suggest that leveraging hybrid AI techniques not only refines brand sentiment monitoring but also provides deeper insights into consumer perceptions and emotional responses. This paper contributes to the field by offering an innovative approach that combines the depth of NLP with the contextual precision of transformers, paving the way for more effective real-time applications in brand management and market analysis.