Leveraging BERT and Sentiment-LSTM for Enhanced AI-Driven Marketing Sentiment Analysis

Abstract

This research paper explores the integration of Bidirectional Encoder Representations from Transformers (BERT) with a Long Short-Term Memory (LSTM) network, specifically designed for sentiment analysis in the field of AI-driven marketing. The study addresses the growing demand for precise sentiment analysis tools capable of interpreting consumer opinions across diverse textual data. By leveraging BERT's sophisticated contextual understanding and LSTM's ability to capture sequential dependencies, the proposed hybrid model significantly enhances the accuracy of sentiment classification in marketing datasets. The methodology involves fine-tuning BERT to extract rich, context-aware embeddings and utilizing these representations as inputs to a sentiment-focused LSTM network, which adapts to the nuances of marketing language and sentiment dynamics. Comprehensive experiments were conducted using both benchmark sentiment datasets and real-world marketing data, demonstrating that the BERT-Sentiment-LSTM model outperforms traditional sentiment analysis approaches, including standalone BERT and LSTM models. Results indicate substantial improvements in F1 scores and overall classification accuracy, highlighting the potential of this hybrid architecture to revolutionize sentiment analysis applications in marketing. Furthermore, the study provides insights into the interpretability of model predictions, essential for actionable marketing strategies. This research contributes to the advancement of AI in sentiment analysis, offering a robust tool for marketers to gauge consumer sentiment with greater precision and reliability.