# Learning Word Ratings for Empathy and Distress from Document-Level User Responses

arXiv: https://arxiv.org/abs/1912.01079 · HTML: https://mlsanity.com/paper/1912.01079/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [Empathic Conversations: A Multi-level Dataset of Contextualized Conversations](https://mlsanity.com/paper/2205.12698/) (2022)
- [EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction](https://mlsanity.com/paper/2102.00272/) (2021)
- [PVG at WASSA 2021: A Multi-Input, Multi-Task, Transformer-Based Architecture for Empathy and Distress Prediction](https://mlsanity.com/paper/2103.03296/) (2021)
- [WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction](https://mlsanity.com/paper/2104.09827/) (2021)
- [A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing](https://mlsanity.com/paper/2210.16604/) (2022)
- [Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation](https://mlsanity.com/paper/2109.07779/) (2021)
- [Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits](https://mlsanity.com/paper/2404.00267/) (2024)
- [EMP-EVAL: A Framework for Measuring Empathy in Open Domain Dialogues](https://mlsanity.com/paper/2301.12510/) (2023)
- [Linguistic traces of stochastic empathy in language models](https://mlsanity.com/paper/2410.01675/) (2024)
- [ER-EDF: A Psychology-Grounded Emotion Regulation Framework for Speech Empathetic Dialogue Generation in Large Audio-Language Models](https://mlsanity.com/paper/2609.15089/) (2026)

## Abstract (arXiv)

Despite the excellent performance of black box approaches to modeling sentiment and emotion, lexica (sets of informative words and associated weights) that characterize different emotions are indispensable to the NLP community because they allow for interpretable and robust predictions. Emotion analysis of text is increasing in popularity in NLP; however, manually creating lexica for psychological constructs such as empathy has proven difficult. This paper automatically creates empathy word ratings from document-level ratings. The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods. We systematically compare a number of approaches to learning word ratings from higher-level supervision against a Mixed-Level Feed Forward Network (MLFFN), which we find performs best, and use the MLFFN to create the first-ever empathy lexicon. We then use Signed Spectral Clustering to gain insights into the resulting words. The empathy and distress lexica are publicly available at: http://www.wwbp.org/lexica.html.