Designing and displaying haptic signals with sensory and emotional attributes can improve the user experience in various applications. Free-form user language provides rich sensory and emotional information for haptic design (e.g., ``This signal feels smooth and exciting''), but little work exists on linking user descriptions to haptic signals (i.e., language grounding). To address this gap, we conducted a study where 12 users described the feel of 32 signals perceived on a surface haptics (i.e., electrovibration) display. We developed a computational pipeline using natural language processing (NLP) techniques, such as GPT-3.5 Turbo and word embedding methods, to extract sensory and emotional keywords and group them into semantic clusters (i.e., concepts). We linked the keyword clusters to haptic signal features (e.g., pulse count) using correlation analysis. The proposed pipeline demonstrates the viability of a computational approach to analyzing haptic experiences. We discuss our future plans for creating a predictive model of haptic experience.

翻译：设计并呈现具有感官与情感属性的触觉信号，能够提升各类应用中的用户体验。自由形式的用户语言为触觉设计提供了丰富的感官与情感信息（例如，“该信号感觉平滑且令人兴奋”），但将用户描述与触觉信号相关联（即语言接地）的研究尚少。为填补这一空白，我们开展了一项研究，让12名用户描述在表面触觉（即电振动）显示器上感知到的32种信号的感觉。我们开发了一个计算流程，利用自然语言处理（NLP）技术（如GPT-3.5 Turbo和词嵌入方法），提取感官与情感关键词并将其分组为语义簇（即概念）。通过相关性分析，我们将关键词簇与触觉信号特征（如脉冲计数）相关联。所提出的流程证明了采用计算方法分析触觉体验的可行性。我们讨论了未来构建触觉体验预测模型的计划。