Enhancing Sentiment Analysis in E-Commerce through the Integration of GPT-3.5 Embeddings and BiLSTM Networks

As e-commerce continues to grow, comprehending consumer emotions has be-come crucial, directly influencing product sales and corporate reputation. Yet, the high dimensionality and complexity of e-commerce platform comment data pose significant challenges to conventional sentiment analysis techniques. In response, this paper presents an innovative sentiment analysis model for e-commerce re-views, leveraging GPT-3.5 embeddings, convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and Self-Attention mechanisms. Initially, GPT-3.5 is employed to transform input text into dynamic word vectors, thereby capturing subtle semantic distinctions. Subsequently, the CNN layer is utilized to extract textual emotional features, while the Self-Attention mechanism enhances the model's sensitivity to subtle emotional shifts. The BiLSTM layer further captures bidirectional, backward and forward contex-tual information, offering a holistic grasp of textual sentiment. Emotional features are ultimately classified using the Softmax function. Relative to existing models, our proposed model exhibits superior performance in e-commerce dataset exper-iments, demonstrating notable enhancements in accuracy, precision, recall, and F1 scores. This validation underscores our solution's effectiveness in conducting sentiment analysis within the e-commerce domain.