The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions (2025)

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 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.

## introduction embodied cognition-the theory that human cognition is rooted in bodily experiences-has gained significant traction across cognitive science, psychology, linguistics, and philosophy. this framework suggests that our bodily experiences shape not only how we interact with our physical environment, but also how we process, represent, and share abstract concepts. a wide range of disciplines have demonstrated that the intersection of cognition and language is deeply intertwined with sensorimotor experiences. for example, a growing pool of research suggests that: individuals learn new concepts better when they can use their bodies to simulate the concepts @xcite ; language processing involves mental simulation of physical actions @xcite ; and emotions emerge from interpretations of physiological signals through a process known as interoception @xcite . in this paper, we ask the question: to what extent are our embodied experiences encoded in, and reflected through, everyday language? to this end, we use the concept of body part mentions (bpms), which we define as instances of language where words referring to parts of the body are used. 1 even though bpms are a relatively simple method for detecting embodiment-related language, we propose them as a useful initial tool for investigating the relationship between language and our embodied selves in everyday language. as such, we use bpms in this paper for an exploratory investigation of how words which are semantically related to the human body can provide interesting insights in natural language use. we introduce two novel bpm corpora, spinn3r bpm (a corpus of blog posts) and tusc bp m (a corpus of tweets), and conduct a variety of experiments to show how they can be used to address three areas of inquiry on the connection between embodiment and natural language. first, we investigate the prevalence of body-part related words in everyday language, as well as how frequency of these words differs across different factors such as medium, gendered pronoun usage, time, and place. second, we look at the relationship between bpms and affect, motivated by the growing pool of research which posits that emotions originate from interpretations of physiological signals. lastly, we propose and test the hypothesis that the degree of prevalence of bpms in social media is indicative of aggregate-level health outcomes. a better understanding of embodiment through language could support work in a range of research areas, especially nlp in health domains (where bpm-heavy corpora are frequent @xcite ). the growing wave of interest in integrating embodiment within computational models of language (especially text-based llms, which rely on bpms to access information about human embodiment, and struggle with many benchmarks of human cognition due to their lack of embodiment @xcite ). all of our code and data is available at our project repository. foot_1 2 related work ## bpm corpora for this work, we investigate two mediums for online, everyday language: blog posts and tweets. we consider each sentence from a blog post and each individual tweet from the tweet corpora as an instance. we created bpm corpora by extracting all instances that included at least one word referring to a body part from the spinn3r personal blog datasets @xcite , and the two tusc datasets: tusc ctry (where tweets are geo-located to either the united states of america or canada) and tusc city (where tweets are geolocated to cities in north america) @xcite . this results in three final bpm corpora: spinn3r bpm , tusc ctry-bpm , and tusc city-bpm . we will refer to the corpora made up of the rest of the instances as spinn3r nobpm and tusc nobp m . we compile a list of body part words by including all the terms in the list used by @xcite , which extracts bpm samples from the spinn3r corpus to annotate samples for the presence of explicitly embodied emotion. 56 additionally, we include plural forms of terms (e.g., hearts, hands, eyes, etc.). we refer to the body part word forms as bp word types. the list of 295 bp word types we used is available on the project webpage. two issues need to be addressed when working with bpm corpora to study embodiment. first, some bp word types are ambiguous (some bpm instances may not actually be referring to a body part: e.g, 'i will be back'). second, it is useful to distinguish between the speaker referring to their own body vs. the speaker referring to someone else's body. we are especially interested in mentions of one's own body as a possibly useful indicator for well-being. while we are interested in the insights that all bpms, including those with a more abstract/metaphorical connections to the human body, can offer on the relationship between embodiment and language, it would also be useful to distinguish instances where the speaker refers directly to their own body parts. and so we use a simple solution that effectively addresses both of the issues raised above. we created three subsets for each bpm corpus that only include the bpm instances preceded by possessive pronouns 'my', 'your', and 'his/her/their', respectively, and call these instances: possessed bpms this helps us create separate corpora for first-, second-, and thirdperson references to body parts, and also excludes a vast majority of mentions that are not explicit references to body parts. for example, 'i will be back' would not be included in any of these corpora, but 'my back hurts' will be. we find that this approach delivers a high number of references to an individual's body part (92% of 100 manually inspected instances). while this approach sacrifices recall by excluding possible references to body parts without possessive pronouns, we benefit from higher precision. 7 additionally, we also conduct some experiments with all bpm instances (the higher-recall and lower-precision corpus). ## research questions about bpms despite substantial evidence from medical research and psychology that points to connections between the mind and the rest of the body, as well as the connection between interoception and emotional well-being, there is little quantitative work using language to explore this connection. in this section and the next, we make use of large amounts of social media data, massive word-emotion lexicons, and the emotion-annotated corpus described in the previous section, to examine questions on how, when, and in what context we refer to our body parts in text (this section) and whether different body parts mentions tend to be used in different emotional contexts (next section). since the questions in this section are relevant to the body, we will index them as b1, b2, etc. the questions in the next section are related to body and affect, so we will index them as ba1, ba2, etc. ## research questions on bpms-affect the primary goal of our work is to explore how language can shed light on the connection between the body, emotion, and well-being. in this section we explore how bpms are associated with emotions. we explore this question using emotions associated with words that co-occur with bpms (using large word-emotion association lexicons) as well as perceived emotions of the speaker (using the new human-annotated spinn3r bpm -zhuang dataset we introduced earlier). in this section, we take a special interest in samples including "@xmath0, since our emphasis is rooted in theories of embodied emotion and health psychology, which suggest that references to one's own body are more likely to reflect internal emotional and physical states. ba1. do posts with body part mentions have markedly different emotional associations? method: this question aims to shed light on whether the relationship between emotion and embodiment manifests in social media text. in this experiment, we look at the proportion of samples (tweets/blog posts) containing at least one word associated with various emotion categories: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust from plutchik's set of emotions @xcite , and high or low valence (positivenegative), arousal (calm-sluggish), and dominance (in control-out of control). we obtain the wordemotion associations from the nrc emotion lexicon @xcite and the nrc vad lexicon @xcite . we compute these proportions for various bpm categories: "@xmath1, "his/her/@xmath2, "@xmath3, as well as the no bpm corpora. results: figure 5 shows the results for valence, arousal and dominance. we find that instances containing bpms have higher percentages of emotionassociated co-occurring words than instances not containing bpms. this is true across 36/42 corpusdimension pairs. we also observe that the jump in scores from the no bpm corpus to bpm corpora is highest for the low-valence and low-dominance dimensions (∼15 percentage points). we find that the bpm category has a statistically significant effect on the percentage of samples containing emotionassociated terms (more details on these tests can be found in appendix i). discussion: referral to one's own body seems to display a strong co-occurrence with emotionassociated language, supporting theories of the connection between embodiment and emotion. when people discuss their own bodies, they tend to use more negative (low valence) emotional language and express less control (low dominance), suggesting these self-references often occur in contexts of pain or powerlessness. in figure 17 in the appendix, we demonstrate that the most frequent words associated with bpms in our corpora support this theory, such as hurt, sore, and sick. ba2. what is the impact of explicitly embodied emotion on the emotions expressed through body part mentions? method: body parts are often referenced as physically involved in emotional responses (e.g., my heart skipped a beat, my stomach dropped). prior work (i.e. zhuang et al.) has assumed that such samples in natural language may be responsible for the emotional associations between emotional and body-related language. in ba1, we showed that bpm instances are more likely to have emotionassociated co-terms than their no-bpm counterparts. with this question we explore whether this increase only exists in explicitly embodied emotion. to do this, we analyze samples which are human-annotated by @xcite as either containing embodied emotion (where a body part physically participates in expressing the emotionannotated as embodied) or not (annotated as non-embodied). specifically, we looked at the degree of emotion-word co-occurrences in the embodied samples and in the non-embodied samples. as a separate and complementary experiment, we manually annotated these exact instances for whether the speaker was feeling any of the emotion categories (anger, fear, joy, sadness, surprise, and trust), by crowdsourcing on amazon mechanical turk. we note a high inter-annotator agreement rate on our human annotations using split-half class match percentage (90.1% on 2 bins to 69.2% on 10 bins). further details on annotator agreement can be found in appendix b. this allows us to determine the extent to which explicitly embodied bpms are more or less emotional than not explicitly embodied bpm instances (non-embodied for short). results: figure 6 shows the percentages of embodied and non-embodied samples where the speaker is experiencing an emotion. figure 7 shows the percentages of embodied and non-embodied samples that include at least one word associated with an emotion. we find that both methods indicate no notable difference in the percentages of emotional samples across explicitly embodied versus non-embodied (or, more precisely, not explicitly embodied) bpm instances (<4 percentage points). in contrast, there is a stark difference between the the emotion percentages of the no bpm samples and the embodied/non-embodied bpm samples ) (>30 percentage points). discussion: the results show that emotional words appear more frequently in bpm sentences, regardless of whether or not the bpms are explicitly written as physically connected to emotion in in the text (explicitly embodied). speakers are also equally likely to be expressing emotion whether the bpm is embodied or not. this unexpected finding supports the theory of constructed emotion and highlights a stronger connection between emotional expression and body-related language than theorized by previous works. ba3. do individual body part mentions co-occur with markedly different emotion distributions? method: in ba1, we looked at the co-occurrence of posts containing "@xmath4. here, we are interested in comparing this average score to posts containing specific "@xmath5. for the most common "@xmath6, we calculated the average proportion of instances that include words associated with specific emotion dimensions. we calculated a mean and standard deviation over these proportions and use these values to find "@xmath7. we also calculated the standard deviation for each emotional category across all common "@xmath8. results: we find that different "@xmath9, and that different profiles of associations for the same type can be found in different corpora. however, some "@xmath10, such as my stomach being most associated with sadness in both tusc ctry-bpm and spinn3r bpm , whereas my chest is most associated with anger. (proportions for emotional word co-occurrence across "@xmath11.) discussion: these results indicate that referral to one's body parts are associated with different affective expressions online. overall trends in t u sc city seem to imply that referral to one's own body parts online often arise from situations of pain, lethargy, and a lack of control. the previous two sections show that bpms are common in online text and they exhibit many systematic and consistent trends across time and region, as well as w.r.t. co-occurrence with emotion words. these results are consistent with what we would expect if bpms are linguistic indicators of one's health. in this section, we directly explore whether, at an aggregate level, the degree of bpms in social media texts correlates with health outcomes. we hypothesize that this occurs because bpms are frequently used online by individuals to express pain or discomfort in their bodies. if so, regional discrepancies in bpm usage may also be correlated with different health outcomes. method: to evaluate this hypothesis, we look at available city-wide health data (city-health-dashboard-dataset, 2025) for all 25 american cities in the tusc city dataset, and correlations between the proportion of regional tweets containing "@xmath12: frequent mental distress, frequent physical distress, life expectancy, and physical inactivity. 8 as points of baseline comparison, we also look at how the health factors investigated are correlated with the number of tweets from each region, and the correlation between the proportion of emotion-associated words (from the nrc emotion/vad lexicons) with the health outcomes studied. results: ## conclusion we created novel corpora designed specifically for the study of body part mentions (bpms), which includes the first-ever dataset of samples explicitly annotated for the emotions of human entities possessing bpms. using these corpora, we answered a series of research questions on the significance of body-related words in everyday language, the relationship between embodiment and emotion, and factors correlated with bpm frequency such as emotional context, time of week/year, and region. we showed that bpms occur frequently in social media texts and have notable temporal and geographic trends. we also showed that bpm instances have markedly higher emotion associations than non-bpm instances-with an especially marked increase in low valence (negativity) and low dominance (helplessness) instances. most notably, through experiments on data from 25 us cities, we showed that the degree of bpm usage can is a powerful indicator of aggregate-level wellbeing. although the connection between language, embodiment, and affect is now well-established, this paper is -to our knowledge -the first-ever approach to understanding this relationship grounded in large amounts of language data. we release our bpm corpus to the public, and hope that our work demonstrates body-related language as a rich and interesting source of material for future nlp research to investigate the deeper connection between language, embodiment, and emotional wellbeing. although bpms are a relatively simple tool for investigating the relationship between embodiment and everyday language, they offer a scalable, interpretable signal for understanding this connection empirically. we hope that our initial exploratory work, through showing that body-related language carries diverse and meaningful associations, emphasizes the richness of studying the intersection of embodiment and natural language.