随着互联网技术的飞速发展和消费者对生鲜产品需求的增长,生鲜电商行业迅速崛起,成为在线购物的重要组成部分。生鲜产品由于其易腐性、保质期短和对运输储存条件的高要求,对物流服务质量提出了更高的挑战。同时,消费者在线评论作为用户...随着互联网技术的飞速发展和消费者对生鲜产品需求的增长,生鲜电商行业迅速崛起,成为在线购物的重要组成部分。生鲜产品由于其易腐性、保质期短和对运输储存条件的高要求,对物流服务质量提出了更高的挑战。同时,消费者在线评论作为用户生成内容的一种形式,对物流服务质量的改进和发展具有显著影响。本研究针对生鲜电商行业的快速发展和消费者需求,提出了面向服务质量改进的生鲜物流评论动机识别策略。通过改进预训练模型增加多层注意力结构,构建基于RoBERTa-HA模型的识别框架,利用深度学习技术对消费者在线评论中的动机进行提取和识别,为电商平台提供服务质量改进的策略,能够为物流服务提供商提供量化的消费者需求和偏好信息。With the rapid development of internet technology and the increasing consumer demand for fresh products, the fresh food e-commerce industry has emerged as a significant component of online shopping. Due to the perishability, short shelf life, and high requirements for transportation and storage conditions of fresh products, the industry faces elevated challenges in ensuring logistics service quality. Meanwhile, consumer online reviews, as a form of user-generated content, play a pivotal role in improving and advancing logistics service quality. This study addresses the rapid growth of the fresh food e-commerce industry and evolving consumer demands by proposing a motivation identification strategy for fresh logistics reviews aimed at service quality enhancement. By introducing a multi-layer attention mechanism to refine pre-trained models, this study develops a recognition framework based on the RoBERTa-HA model. Leveraging deep learning techniques, it extracts and identifies consumer motivations embedded in online reviews, providing e-commerce platforms with actionable strategies to improve service quality. The proposed approach also offers logistics service providers quantitative insights into consumer needs and preferences.展开更多
文摘随着互联网技术的飞速发展和消费者对生鲜产品需求的增长,生鲜电商行业迅速崛起,成为在线购物的重要组成部分。生鲜产品由于其易腐性、保质期短和对运输储存条件的高要求,对物流服务质量提出了更高的挑战。同时,消费者在线评论作为用户生成内容的一种形式,对物流服务质量的改进和发展具有显著影响。本研究针对生鲜电商行业的快速发展和消费者需求,提出了面向服务质量改进的生鲜物流评论动机识别策略。通过改进预训练模型增加多层注意力结构,构建基于RoBERTa-HA模型的识别框架,利用深度学习技术对消费者在线评论中的动机进行提取和识别,为电商平台提供服务质量改进的策略,能够为物流服务提供商提供量化的消费者需求和偏好信息。With the rapid development of internet technology and the increasing consumer demand for fresh products, the fresh food e-commerce industry has emerged as a significant component of online shopping. Due to the perishability, short shelf life, and high requirements for transportation and storage conditions of fresh products, the industry faces elevated challenges in ensuring logistics service quality. Meanwhile, consumer online reviews, as a form of user-generated content, play a pivotal role in improving and advancing logistics service quality. This study addresses the rapid growth of the fresh food e-commerce industry and evolving consumer demands by proposing a motivation identification strategy for fresh logistics reviews aimed at service quality enhancement. By introducing a multi-layer attention mechanism to refine pre-trained models, this study develops a recognition framework based on the RoBERTa-HA model. Leveraging deep learning techniques, it extracts and identifies consumer motivations embedded in online reviews, providing e-commerce platforms with actionable strategies to improve service quality. The proposed approach also offers logistics service providers quantitative insights into consumer needs and preferences.
