电子商务是数字经济中的重要组成部分，具有广阔的发展空间和创新活跃度。作为实体经济数字化转型的先锋，电子商务的蓬勃发展为生鲜企业带来了前所未有的机遇，开辟了新的销售渠道，有效提升了企业的市场曝光度和销售业绩；同时让消费者拥有了更多的选择，更加注重价格和品质。在竞争激烈的市场环境下，以需求为导向，考虑消费者行为属性进行动态定价，以适应不断变化的消费者需求和市场状况，是促进零售商长足发展的关键。在线文本评论是反应消费者行为、影响消费者购买决策的关键要素，基于此，本文以在线文本评论情感分析在有机蔬菜定价中的应用为例，以提高有机蔬菜销售效益和消费者满意度为研究目标，以探索在线文本评论情感分析与消费者购买决策的相互作用为突破口，围绕“情感分析有效性—消费者价值评估—定价策略适应性”的有机蔬菜定价要求，运用协同训练算法、消费者行为理论和交替方向乘子法，开展基于在线文本评论情感分析的有机蔬菜定价研究。具体研究工作如下：

（1）针对在线文本评论蕴含的情感具有复合性、隐蔽性、层次性等多种特征，本文提出了融合多分类模糊支持向量机和Tri-training算法的文本评论情感分析方法。首先，通过使用半梯半岭分布函数来刻画模糊隶属度，利用多分类模糊支持向量机对标记样本进行精细化分类，提升初始分类器的性能；其次，结合Tri-training算法，采用多个分类器协同训练的方式，增强模型的泛化能力；最后，采用软投票集成规则，确保不同分类器之间的权重分配合理，进一步提高情感分析的准确性，为实际应用中的情感分析任务提供了切实可行的解决方案。

（2）针对在线文本评论对消费者购买行为和零售商定价决策的影响，以提高有机蔬菜销售效益和消费者满意度为研究目标，研究基于在线文本评论情感分析的有机蔬菜动态定价问题。首先，深入剖析在线文本评论中情感倾向与消费者行为的内在联系，挖掘消费者对有机蔬菜的满意度、购买意愿以及价格敏感度等多维度信息；其次，综合考虑市场供需、消费者偏好、有机蔬菜易腐性等关键影响因素，构建了基于在线文本评论情感分析的有机蔬菜动态定价模型，旨在最大化有机蔬菜零售商利润；最后，运用融合高斯回代的交替方向乘子法对模型求解，并以京东平台上的有机西红柿在线文本评论为例验证模型与算法的有效性，为有机蔬菜零售商提供了可行的管理建议。

本文研究成果，有利于促进自然语言处理等信息资源管理理论方法与电商运营管理实践的交叉、渗透和融合，为生鲜电商运营管理提供了新的视角和方向，对挖掘数据价值潜力，降低有机蔬菜运营成本，提高电商行业经济效益，深入推进数字经济高质量发展，具有重要的理论价值和现实意义。

As an important component of the digital economy, e-commerce has vast development space and high level of innovation activity. As a vanguard for the digital transformation of the real economy, the thriving development of e-commerce has brought unprecedented opportunities for fresh enterprises, opening up new sales channels and effectively improving their market exposure and sales performance. At the same time, consumers have more choices and pay more attention to price and quality. In a fiercely competitive market environment, focusing on demand, considering consumer behavioral attributes for dynamic pricing to adapt to the constantly changing consumer demands and market conditions is key to promoting significant development for retailers. Online text reviews play a significant role in consumers' purchasing decision-making processes.

This thesis takes the application of sentiment analysis of online text reviews in organic vegetable pricing as an example, with the research goal of improving the sales effectiveness and consumer satisfaction of organic vegetables. This thesis explores the interaction between online text review sentiment analysis and consumer purchasing decisions as a breakthrough, focusing on the requirements of organic vegetable pricing. By using collaborative training algorithms, consumer behavior theories, and alternating direction multiplier method, the research is conducted on organic vegetable pricing based on sentiment analysis of online text reviews. The specific research work is as follows:

(1) This thesis proposes a method for analyzing the sentiment of text comments by integrating the Multi-Classification Fuzzy Support Vector Machine and Tri-training algorithm, aiming at the fact that the sentiment embedded in online text comments has various characteristics such as composite, hidden, hierarchical, etc. Firstly, by using the half-trapezoidal and half-ridge distribution function to portray the fuzzy affiliation degree, the multiclassification fuzzy support vector machine is used to refine the classification of labeled samples and improve the performance of the initial classifiers. Secondly, combined with the Tri-training algorithm, multiple classifiers are trained in a cooperative manner to enhance the model's generalization ability. Finally, the soft-voting integration rule is used to ensure that the weights between different classifiers are distribution is reasonable to further improve the accuracy of sentiment analysis, which provides a practical solution for the task of sentiment analysis in practical applications.

(2) This thesis aims to improve the sales efficiency and consumer satisfaction of organic vegetables by addressing the impact of online text reviews on consumer purchasing behavior and retailer pricing decisions. Based on the analysis of online text review sentiment analysis, this study investigates dynamic pricing issues for organic vegetables. First, we delve into the intrinsic connections between emotional tendencies in online text reviews and consumer behavior, exploring multiple dimensions of information such as consumer satisfaction, willingness to purchase, and price sensitivity towards organic vegetables. Second, we consider key influencing factors such as market supply and demand, consumer preferences, and perishability of organic vegetables, and construct a dynamic pricing model for organic vegetables based on online text review sentiment analysis. The model aims to maximize the profits of organic vegetable retailers. Finally, we use the Gauss-Seidel backward method integrated with the alternating direction multiplier method to solve the model, and obtain the optimal dynamic pricing strategy. Taking online text reviews of organic tomatoes on the JD platform as an example, we verify the effectiveness of the model and algorithm, providing feasible management suggestions for organic vegetable retailers.

The research results of this thesis are conducive to promoting the intersection, penetration and integration of natural language processing and other information resource management theories and methods with e-commerce operation and management practices, providing a new perspective and direction for fresh food e-commerce operation and management, tapping the potential of the value of the data, reducing the operating costs of organic vegetables, and improving the economic efficiency of the e-commerce industry, promoting the high-quality development of the digital economy, which is of significant theoretical value and practical significance.

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