In opinion mining of product reviews, an important task is to provide a summary of customers＇ opinions based on different opinion targets. Due to various knowledge backgrounds or linguistic habits, customers use a va...In opinion mining of product reviews, an important task is to provide a summary of customers＇ opinions based on different opinion targets. Due to various knowledge backgrounds or linguistic habits, customers use a variety of terms to describe the same opinion target. These terms are called as context-dependent synonyms. In order to provide a comprehensive summary, the first step is to classify these opinion target words into groups. In this article, we mainly focus on clustering context-dependent opinion target words in Chinese product reviews. We utilize three clustering methods based on distributional similarity and use four different co-occurrence matrices for experiments. According to the experimental results on a large number of reviews, we find that our proposed heuristic k-means clustering method using opinion target words co-occurrence matrix achieves the best clustering result with lower time complexity and less memory space. In addition, the accuracy is more stable when choosing different combinations of centroids. For some kinds of co-occurrence matrices, we also find that using small-size （low-dimensional） matrices achieves higher average clustering accuracy than using large-size （high-dimensional） matrices. Our findings provide a time-efficient and space-efficient way to cluster opinion targets with high accuracy.展开更多
Air pollution negatively impacts food security. This paper reviews the current literature on the relationship between air pollution and food security from the perspective of food system. It highlights that agricultura...Air pollution negatively impacts food security. This paper reviews the current literature on the relationship between air pollution and food security from the perspective of food system. It highlights that agricultural emissions which substantially contribute to air pollution could happen at every stage along the food supply chain. Meanwhile, air pollution can not only affect plant growth and animal health but also shift market equilibrium of both agro-inputs and outputs in the food supply chain and thereby affect food security indirectly. Furthermore, this study evaluates the effects of agricultural policy and energy policy on food security and air pollution, respectively, and provides an overview of potential policy instruments to reduce air pollution while ensuring food security. Finally, we identify the remaining research and policy issues for further studies, mainly focusing on the study of household＇s bounded rational behaviors and the issue of rural aging population.展开更多
针对顾客产品偏好快速变化对企业分析和预测顾客偏好能力的要求,提出一种面向产品改进的顾客偏好分析与预测方法,首先构建长短期记忆网络模型,预测产品设计迭代期间的情感值和重要度,并计算预测准确度;然后通过基于产品特征情感变化模...针对顾客产品偏好快速变化对企业分析和预测顾客偏好能力的要求,提出一种面向产品改进的顾客偏好分析与预测方法,首先构建长短期记忆网络模型,预测产品设计迭代期间的情感值和重要度,并计算预测准确度;然后通过基于产品特征情感变化模式的产品设计改进模型判断各个特征的变化模式,明确待改进的产品特征及改进优先级;最后以DJI Mini 2无人机的在线评论为例验证了方法的有效性。展开更多
基金the Commonweal Technical Project of Zhejiang Province of China under Grant No. 2013C33063, the National Natural Science Foundation of China under Grant Nos. 61100183, 61402417, the Natural Science Foundation of Zhejiang Province of China under Grant No. LQ13F020014, and the 521 Talents Project of Zhejiang Sci-Tech University.
文摘In opinion mining of product reviews, an important task is to provide a summary of customers＇ opinions based on different opinion targets. Due to various knowledge backgrounds or linguistic habits, customers use a variety of terms to describe the same opinion target. These terms are called as context-dependent synonyms. In order to provide a comprehensive summary, the first step is to classify these opinion target words into groups. In this article, we mainly focus on clustering context-dependent opinion target words in Chinese product reviews. We utilize three clustering methods based on distributional similarity and use four different co-occurrence matrices for experiments. According to the experimental results on a large number of reviews, we find that our proposed heuristic k-means clustering method using opinion target words co-occurrence matrix achieves the best clustering result with lower time complexity and less memory space. In addition, the accuracy is more stable when choosing different combinations of centroids. For some kinds of co-occurrence matrices, we also find that using small-size （low-dimensional） matrices achieves higher average clustering accuracy than using large-size （high-dimensional） matrices. Our findings provide a time-efficient and space-efficient way to cluster opinion targets with high accuracy.
基金funding supports from the National Natural Science Foundation of China (NSFC, 71473123 and 71633005)the German Research Foundation (DFG, RTG1666)
文摘Air pollution negatively impacts food security. This paper reviews the current literature on the relationship between air pollution and food security from the perspective of food system. It highlights that agricultural emissions which substantially contribute to air pollution could happen at every stage along the food supply chain. Meanwhile, air pollution can not only affect plant growth and animal health but also shift market equilibrium of both agro-inputs and outputs in the food supply chain and thereby affect food security indirectly. Furthermore, this study evaluates the effects of agricultural policy and energy policy on food security and air pollution, respectively, and provides an overview of potential policy instruments to reduce air pollution while ensuring food security. Finally, we identify the remaining research and policy issues for further studies, mainly focusing on the study of household＇s bounded rational behaviors and the issue of rural aging population.
文摘针对顾客产品偏好快速变化对企业分析和预测顾客偏好能力的要求,提出一种面向产品改进的顾客偏好分析与预测方法,首先构建长短期记忆网络模型,预测产品设计迭代期间的情感值和重要度,并计算预测准确度;然后通过基于产品特征情感变化模式的产品设计改进模型判断各个特征的变化模式,明确待改进的产品特征及改进优先级;最后以DJI Mini 2无人机的在线评论为例验证了方法的有效性。
文摘为提升客户对产品的认可度,针对传统概念设计阶段未充分考虑客户感性需求偏好及客户感性需求获取困难的问题,提出一种在线评论数据驱动的客户感性需求识别及向设计特征映射方法。首先,基于形态学分析法,通过构建能量材料信号(Energy Material Signal, EMS)模型试图从产品中发现所有设计特征,并基于在线评论数据从所有设计特征中筛选出用户关注的核心特征;其次,从在线评论数据中提取形容词组成感性词对,以感性词作为中心词并利用词向量技术获得非中心词,基于情感词典,利用所给方法计算产品感性词对的感性评价值;然后,基于数量化理论Ⅰ(Quantitative TheoryⅠ,QTⅠ)建立客户感性评价与产品设计元素之间的映射模型,并为改进产品设计提供依据;最后,通过实例验证所提方法的可行性和有效性。
