面向KANO模型的文本情绪强度分布挖掘方法
Mining Method of Text Emotion Intensity Distribution for KANO Model
摘要: Web2.0时代，用户习惯于在线上自由地分享体验过程中的感受和评价，这为KANO模型提供了比调查问卷更加便捷和真实的数据来源。然而挖掘用户的情绪体验是运用用户生成内容构建KANO模型的关键前提。本文以情绪轮理论为基础，结合了词向量和长短时记忆神经网络，提出了文本情绪强度分布预测模型，并且通过通用的文本表示方法，探索模型的跨领域能力，最终为预测用户的情绪强度分布提供一种满意的挖掘方法。本文研究为KANO模型中衡量用户体验质量提供了新的基于情绪的视角，并且为从用户生成文本内容中构建KANO模型奠定了基础。
Abstract: In the Web2.0 era, users are accustomed to freely sharing the feelings and evaluations during the experience process, which provides a more convenient and authentic data source for the KANO model than the classic method of questionnaire. However, mining the user’s emotional experience is a key prerequisite for building a KANO model using user-generated content. Based on the theory of emotion wheel, this paper proposes a text emotion intensity distribution prediction model by combining word vectors and long-term and short-term memory neural networks, and explores the model’s cross-domain capabilities through a universal text representation method, and finally provide a satisfactory mining method to predict the users’ emotion intensity distribution. This study provides a new emotion-based perspective for measuring the quality of user experience in the KANO model, and lays the foundation for constructing the KANO model from user-generated text content.
文章引用：马彪, 李想. 面向KANO模型的文本情绪强度分布挖掘方法[J]. 服务科学和管理, 2020, 9(1): 61-71. https://doi.org/10.12677/SSEM.2020.91008

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