# Grounding Emotional Descriptions to Electrovibration Haptic Signals

arXiv: https://arxiv.org/abs/2411.02118 · HTML: https://mlsanity.com/paper/2411.02118/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [A Review of Surface Haptics:Enabling Tactile Effects on Touch Surfaces](https://mlsanity.com/paper/2004.13864/) (2020)

## Built on by (most cited first, 2 of 2)

- [HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals](https://mlsanity.com/paper/2507.13318/) (2025)
- [HapticLLaMA: A Multimodal Sensory Language Model for Haptic Captioning](https://mlsanity.com/paper/2508.06475/) (2025)

## Abstract (arXiv)

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.