Enhancing Marketing Strategies through AI-Powered Sentiment Analysis: Utilizing BERT, LSTM, and Sentiment Lexicon Approaches

Abstract

This research paper explores the integration of artificial intelligence (AI) into marketing strategies, with a specific focus on sentiment analysis to refine and enhance consumer engagement. We investigate the efficacy of using advanced AI models such as BERT (Bidirectional Encoder Representations from Transformers), LSTM (Long Short-Term Memory networks), and sentiment lexicons to analyze large volumes of consumer feedback from social media platforms and online reviews. Our study first outlines the theoretical foundations of sentiment analysis and its relevance to marketing strategies. We then conduct an empirical analysis, comparing the performance of BERT, LSTM, and lexicon-based approaches in terms of accuracy, efficiency, and scalability for sentiment detection. The findings indicate that BERT outperforms LSTM and lexicon models in capturing nuanced sentiment expressions due to its deep contextual understanding, while LSTM provides competitive results with faster computation times. Lexicon approaches, although less effective for complex texts, offer greater interpretability and ease of implementation. The paper discusses how these AI-powered methods can inform and transform marketing strategies by providing marketers with deeper insights into consumer opinions and emotional responses. We also address the challenges of deploying AI in marketing, including data privacy concerns and the need for domain-specific model tuning. The research concludes with recommendations for integrating AI-driven sentiment analysis into marketing practices, thereby enabling businesses to respond dynamically to customer sentiments and improve brand perception, customer satisfaction, and competitive advantage.