Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis - Liu, Yuzhi; Ding, Zhong (Frontiers in Psychology, 2022)
Electronic Commerce (E-Commerce) through digital platforms relies on diverse user features to provide a better user experience. In particular, the user experience and connection between digital platforms are exploited through semantic emotions. This provides a personalized recommendation for different user categories across the E-Commerce platforms. This manuscript introduces a Syntactic Data Inquiring Scheme (SDIS) to strengthen the semantic analysis. This scheme first identifies the emotional data based on user comments and repetition on the E-Commerce platform. The identifiable and non-identifiable emotion data is classified using positive and repeated comments using the deep learning paradigm. This classification attunes the recommendation system for providing best-afford user services through product selection, ease of access, promotions, etc. The user interest and recommendation factors are classified and trained for further promotions/ recommendations in the learning process. The proposed scheme’s performance is analyzed through appropriate experimental considerations.