RETRACTED: Sentiment Analysis and Consumer Purchase Intention Prediction Based on BERT and DBLSTM

Le résumé fourni par la source

With the rapid growth of e-commerce and social media, analyzing consumer sentiment and predicting purchase intentions are vital for understanding behavior and optimizing marketing strategies. This study proposes an integrated model combining Bidirectional Encoder Representation from Transformers (BERT) and Deep Bidirectional Long Short-Term Memory Network (DBLSTM). BERT efficiently extracts semantic features from consumer reviews using self-attention mechanisms, while DBLSTM captures sequential dynamics, leveraging temporal dependencies for predicting purchase intentions. The model was evaluated on real-world datasets and compared against other deep learning models. Results showed that the BERT-DBLSTM model outperformed others in sentiment analysis accuracy and purchase intention prediction, demonstrating higher generalization and prediction accuracy. This approach provides enterprises with precise market insights, enabling improved marketing strategies, enhanced user satisfaction, and increased conversion rates.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.