# Best Practices in the Creation and Use of Emotion Lexicons

arXiv: https://arxiv.org/abs/2210.07206 · HTML: https://mlsanity.com/paper/2210.07206/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 72 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation](https://mlsanity.com/paper/1712.01765/) (2017)
- [Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling](https://mlsanity.com/paper/1712.01741/) (2017)
- [Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/2109.08256/) (2021)
- [Ethics Sheets for AI Tasks](https://mlsanity.com/paper/2107.01183/) (2021)
- [Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons](https://mlsanity.com/paper/2011.03492/) (2020)
- [What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study](https://mlsanity.com/paper/2110.04845/) (2021)
- [SOLO: A Corpus of Tweets for Examining the State of Being Alone](https://mlsanity.com/paper/2006.03096/) (2020)

## Built on by (most cited first, 27 of 27)

- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [NRC VAD Lexicon v2: Norms for Valence, Arousal, and Dominance for over 55k English Terms](https://mlsanity.com/paper/2503.23547/) (2025)
- [Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://mlsanity.com/paper/2306.02213/) (2023)
- [Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers](https://mlsanity.com/paper/2310.17369/) (2023)
- [WorryWords: Norms of Anxiety Association for over 44k English Words](https://mlsanity.com/paper/2411.03966/) (2024)
- [SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection](https://mlsanity.com/paper/2503.07269/) (2025)
- [Emotion Granularity from Text: An Aggregate-Level Indicator of Mental Health](https://mlsanity.com/paper/2403.02281/) (2024)
- [Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarce](https://mlsanity.com/paper/2410.12691/) (2024)
- [Words of Warmth: Trust and Sociability Norms for over 26k English Words](https://mlsanity.com/paper/2506.03993/) (2025)
- [SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://mlsanity.com/paper/2604.07066/) (2026)
- [Affect, Body, Cognition, Demographics, and Emotion: The ABCDE of Text Features for Computational Affective Science](https://mlsanity.com/paper/2512.17752/) (2025)
- [Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across Age](https://mlsanity.com/paper/2306.05387/) (2023)
- [The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions](https://mlsanity.com/paper/2505.16189/) (2025)
- [Breaking Bad: Norms for Valence, Arousal, and Dominance for over 10k English Multiword Expressions](https://mlsanity.com/paper/2511.19816/) (2025)
- [DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2601.23022/) (2026)
- [Affect as a proxy for literary mood](https://mlsanity.com/paper/2304.02894/) (2023)
- [A Cost-aware Study of Depression Language on Social Media using Topic and Affect Contextualization](https://mlsanity.com/paper/2306.17564/) (2023)
- [UoB-NLP at SemEval-2025 Task 11: Leveraging Adapters for Multilingual and Cross-Lingual Emotion Detection](https://mlsanity.com/paper/2504.08543/) (2025)
- [University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection](https://mlsanity.com/paper/2505.16460/) (2025)
- [EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian](https://mlsanity.com/paper/2505.23297/) (2025)
- [CSIRO-LT at SemEval-2025 Task 11: Adapting LLMs for Emotion Recognition for Multiple Languages](https://mlsanity.com/paper/2508.01161/) (2025)
- [Annotating Dimensions of Social Perception in Text: A Sentence-Level Dataset of Warmth and Competence](https://mlsanity.com/paper/2601.06316/) (2026)
- [When are We Worried? Temporal Trends of Anxiety and What They Reveal about Us](https://mlsanity.com/paper/2602.10400/) (2026)
- [From Trial by Fire To Sleep Like a Baby: A Lexicon of Anxiety Associations for 20k English Multiword Expressions](https://mlsanity.com/paper/2602.18692/) (2026)
- [Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features](https://mlsanity.com/paper/2602.22846/) (2026)

## Abstract (arXiv)

Words play a central role in how we express ourselves. Lexicons of word-emotion associations are widely used in research and real-world applications for sentiment analysis, tracking emotions associated with products and policies, studying health disorders, tracking emotional arcs of stories, and so on. However, inappropriate and incorrect use of these lexicons can lead to not just sub-optimal results, but also inferences that are directly harmful to people. This paper brings together ideas from Affective Computing and AI Ethics to present, some of the practical and ethical considerations involved in the creation and use of emotion lexicons -- best practices. The goal is to provide a comprehensive set of relevant considerations, so that readers (especially those new to work with emotions) can find relevant information in one place. We hope this work will facilitate more thoughtfulness when one is deciding on what emotions to work on, how to create an emotion lexicon, how to use an emotion lexicon, how to draw meaningful inferences, and how to judge success.