Optimizing Sentiment Analysis of User Reviews and Emotional Marketing Strategies on E-commerce Platforms Using Deep Learning Algorithms

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Yawen Li


In the dynamic landscape of e-commerce, understanding user sentiment and leveraging emotional marketing strategies are pivotal for enhancing customer engagement and driving business success. This study investigates the optimization of sentiment analysis techniques and the implementation of emotional marketing strategies using deep learning algorithms on e-commerce platforms. Through the analysis of user-generated content, particularly reviews, a dataset encompassing a diverse range of products and brands was collected. Deep learning models, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based architectures like BERT, were employed to perform sentiment analysis on the dataset. The models exhibited high accuracy, precision, recall, and F1 scores across positive, negative, and neutral sentiment categories, highlighting their effectiveness in capturing nuanced sentiment expressions. Furthermore, emotion detection techniques, leveraging lexicon-based approaches and machine learning classifiers, were utilized to identify emotional states expressed in user reviews. The results demonstrated satisfactory accuracy and mean squared error, indicating the model's ability to discern emotional nuances in textual content. A/B testing experiments were conducted to evaluate the effectiveness of emotional marketing strategies in driving user engagement and conversion actions. Significant differences were observed in click-through rates (CTR) and conversion rates (CR) between different marketing variations, emphasizing the impact of emotional content on user behaviour. this study contributes to the advancement of sentiment analysis and emotional marketing strategies in e-commerce, providing valuable insights for businesses aiming to cultivate meaningful connections with their customers and achieve sustainable growth in the digital marketplace.

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