| Challenge: | Emotion lexicons describe the affective meaning of words but are limited in coverage for most languages. |
| Approach: | They propose a method for creating arbitrarily large emotion lexicons for any target language. |
| Outcome: | The proposed method exceeds human reliability for some languages and variables. |
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Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons (L18-1)
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| Challenge: | Existing representational frameworks for emotion encoding are incompatible with semantic polarity, resulting in a large amount of incompatible emotion lexicons. |
| Approach: | They propose to map different emotion representation formats onto each other for mutual compatibility and interoperability of language resources. |
| Outcome: | The proposed method produces (near-)gold quality emotion lexicons even in crosslingual settings. |
Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (2020.coling-main)
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| Challenge: | Emotion lexicons provide information about associations between words and emotions. |
| Approach: | They use crowdsourcing to annotate words with Plutchik's 8 basic emotions, providing binary labels. |
| Outcome: | The proposed lexicons provide information about associations between words and emotions . the lexiconics are useful in emotional analyses of reviews, literary texts, and posts on social media . |
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin. (2020.lrec-1)
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| Challenge: | a new paper aims to provide sentiment analysis tools for ancient languages . the current sentiment analysis resources only cover modern languages based on textual typologies . |
| Approach: | They propose to use manually-curated Latin lexicons to evaluate sentiment analysis tools . they propose a gold standard and a silver standard for evaluating lexical items . |
| Outcome: | The proposed lexicons are evaluated using a gold standard and a silver standard for sentiment analysis. |
Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Emotion arcs capture how an individual (or a population) feels over time. |
| Approach: | They compare machine-learning and Lexicon-Only methods to generate emotion arcs . they run experiments on 18 diverse datasets in 9 languages . |
| Outcome: | The proposed method is poor at instance level emotion classification, but highly accurate when aggregating information from hundreds of instances. |
Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
| Approach: | They propose to use unsupervised, supervised, and finally supervised methods to extract emotional associations from pretrained vectors and models. |
| Outcome: | The proposed method shows higher correlation with ground truth ratings than state-of-the-art lexicons based on labeled data. |
Hard Emotion Test Evaluation Sets for Language Models (2025.findings-naacl)
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| Challenge: | Existing tests on emotion datasets do not show whether language models understand emotions or exploit supperficial lexical cues. |
| Approach: | They propose to use two existing emotion datasets to evaluate whether language models make inferential decisions for emotion detection. |
| Outcome: | The proposed test sets evaluate language models on emotion datasets. |
Best Practices in the Creation and Use of Emotion Lexicons (2023.findings-eacl)
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| Challenge: | Inappropriate and incorrect use of emotion lexicons can lead to harmful inferences . |
| Approach: | They propose to present some of the practical and ethical considerations involved in the creation and use of emotion lexicons. |
| Outcome: | The proposed lexicons can lead to harmful inferences and sub-optimal results . the aim is to provide a comprehensive set of practical and ethical considerations . |
Characterizing and Evaluating Working Emotion Vocabularies in Multilingual Large Language Models (2026.acl-long)
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| Challenge: | Prior work evaluating emotion and affective understanding in large language models rely on predetermined label sets or focus on a singular evaluation task. |
| Approach: | They examine the ability of multilingual language models to predict any term used by an author to label their own feelings or emotions. |
| Outcome: | The proposed models perform poorly on three different tasks in English and Spanish. |
A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse Corpus (2022.lrec-1)
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| Challenge: | Using a linguistic perspective, emotion annotation is considered a difficult task because of the lack of consensus on emotional categories, the fuzziness of boundaries between them or the great variability of emotion expressions types. |
| Approach: | They propose a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in french. |
| Outcome: | The proposed method clarifies the main concepts implied by the analysis of emotions as they are expressed in texts and performs a manual annotation campaign on a corpus of 1,594 texts (ca. 515K tokens) of different genres. |
Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level (C18-1)
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| Challenge: | Emotion Representation Mapping (ERM) is an alternative to Word Emotion Induction (WEI) for automatic emotion lexicon construction. |
| Approach: | They propose a neural network approach to ERM that converts existing emotion ratings from one representation format into another by mapping Valence-Arousal-Dominance annotations into Ekman’s Basic Emotions. |
| Outcome: | The proposed model outperforms the state-of-the-art in 13 languages and is almost as reliable as human annotations even in cross-lingual settings. |