Leveraging BERT and LSTM for Enhanced Sentiment Analysis in Marketing Campaigns

Abstract

This research paper explores the integration of Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) networks to enhance sentiment analysis in marketing campaigns. Sentiment analysis is crucial for understanding consumer feedback and optimizing marketing strategies. Traditional models often struggle with contextually rich and nuanced language, which can lead to inaccurate sentiment classification. This study proposes a hybrid model leveraging BERT's superior contextual embedding capabilities and LSTM's proficiency in sequential data processing to improve sentiment detection accuracy. We conducted comprehensive experiments using multiple datasets from various marketing campaigns, comparing the performance of the proposed BERT-LSTM model against baseline models such as conventional LSTM, BERT alone, and other state-of-the-art sentiment analysis models. Results indicate that the BERT-LSTM model consistently outperforms these baselines, achieving significant improvements in accuracy, precision, and recall. Furthermore, the model demonstrates robustness across different contexts and languages, suggesting broad applicability in global marketing environments. This research highlights the potential of advanced natural language processing techniques to refine sentiment analysis, offering marketers enhanced tools for campaign assessment and strategy development. The findings underscore the importance of adopting hybrid approaches to capitalize on the strengths of different machine learning models, ultimately driving more informed decision-making in the marketing domain.