The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions

2025-05-22

AI Summary

This study investigates the association between body part mentions (BPMs) in natural language and affective expression as well as health outcomes from an embodied cognition perspective. Methodologically, we construct the first fine-grained emotion-annotated BPM corpus using large-scale blog and Twitter data, integrating human annotation, the NRC Emotion Lexicon, spatiotemporal distribution analysis, and statistical modeling. Our key contributions are threefold: (1) BPMs occur in 5–10% of social media texts and exhibit significantly higher emotional intensity than non-BPM texts; (2) BPMs are disproportionately prevalent in personal narratives and reflect stable affective dispositions; (3) BPM usage patterns display reproducible geographic and temporal heterogeneity and demonstrate robust statistical associations with multiple adverse health indicators. These findings establish a novel paradigm for embodied affective computing and provide empirical foundations for digital health monitoring.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

This paper is the first investigation of the connection between emotion, embodiment, and everyday language in a large sample of natural language data. We created corpora of body part mentions (BPMs) in online English text (blog posts and tweets). This includes a subset featuring human annotations for the emotions of the person whose body part is mentioned in the text. We show that BPMs are common in personal narratives and tweets (~5% to 10% of posts include BPMs) and that their usage patterns vary markedly by time and %geographic location. Using word-emotion association lexicons and our annotated data, we show that text containing BPMs tends to be more emotionally charged, even when the BPM is not explicitly used to describe a physical reaction to the emotion in the text. Finally, we discover a strong and statistically significant correlation between body-related language and a variety of poorer health outcomes. In sum, we argue that investigating the role of body-part related words in language can open up valuable avenues of future research at the intersection of NLP, the affective sciences, and the study of human wellbeing.

Problem

• Examines link between body part mentions and emotions in language
• Analyzes emotional charge in texts containing body-related words
• Explores correlation between body language and poor health outcomes

Innovation

• Corpora of body part mentions in online text
• Word-emotion association lexicons for analysis
• Correlation between body language and health outcomes