# Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level

arXiv: https://arxiv.org/abs/1806.08890 · HTML: https://mlsanity.com/paper/1806.08890/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [Supporting Cognitive and Emotional Empathic Writing of Students](https://mlsanity.com/paper/2105.14815/) (2021)
- [Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality](https://mlsanity.com/paper/2205.08939/) (2022)
- [What Drives the Use of Metaphorical Language? Negative Insights from Abstractness, Affect, Discourse Coherence and Contextualized Word Representations](https://mlsanity.com/paper/2205.11113/) (2022)
- [Real-Time Online Stock Forecasting Utilizing Integrated Quantitative and Qualitative Analysis](https://mlsanity.com/paper/2311.15218/) (2023)
- [Guilt by Association: Emotion Intensities in Lexical Representations](https://mlsanity.com/paper/2104.08679/) (2021)
- [Emotion Embeddings unicodex2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets](https://mlsanity.com/paper/2308.07871/) (2023)
- [Multi-refined Feature Enhanced Sentiment Analysis Using Contextual Instruction](https://mlsanity.com/paper/2511.00537/) (2025)
- [Mixed Feelings, Single Polarity](https://mlsanity.com/paper/2610.15977/) (2026)

## Abstract (arXiv)

Emotion Representation Mapping (ERM) has the goal to convert existing emotion ratings from one representation format into another one, e.g., mapping Valence-Arousal-Dominance annotations for words or sentences into Ekman's Basic Emotions and vice versa. ERM can thus not only be considered as an alternative to Word Emotion Induction (WEI) techniques for automatic emotion lexicon construction but may also help mitigate problems that come from the proliferation of emotion representation formats in recent years. We propose a new neural network approach to ERM that not only outperforms the previous state-of-the-art. Equally important, we present a refined evaluation methodology and gather strong evidence that our model yields results which are (almost) as reliable as human annotations, even in cross-lingual settings. Based on these results we generate new emotion ratings for 13 typologically diverse languages and claim that they have near-gold quality, at least.