Semantic Understanding Model in Computer Natural Language Processing

• Published in: 2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE) pp. 234 - 238
• Main Author: Sun, Taotao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.03.2025
• Subjects: Accuracy; Analytical models; Computational modeling; Computer; Deep learning; Electronic commerce; Face recognition; Force; Natural language processing; Semantic understanding model; Semantics; Sentiment analysis; Solid modeling
• DOI: 10.1109/EDPEE65754.2025.00045

Live streaming e-commerce, as an emerging force in the field of e-commerce, has attracted widespread attention from all sectors of society. Behind the prosperity of live streaming e-commerce is the massive amount of comment data generated every moment. These data not only record consumers' purchasing behavior, but also contain their true feelings and evaluations of the product, thus having high research and commercial value. To explore the potential of this data in depth, natural language processing (NLP) technology has emerged. NLP can efficiently process and analyze massive amounts of comment text, revealing users' purchasing experience, feelings, and comment characteristics. On this basis, this article proposes a semantic understanding model based on deep learning (DL), aiming to further analyze the sentiment of user comments. This model, through the DL algorithm, can more accurately capture the emotional tendencies in comments, thereby helping businesses better understand consumer needs and satisfaction. The experimental results show that the model has achieved significant results in sentiment analysis tasks, not only improving the accuracy of analysis, but also providing more refined user profiles and marketing strategy recommendations for e-commerce enterprises.