mlsanityData to 7 Oct 2026

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

arXiv 2109.08256 ↗First posted 17 Sep 2021cs.CL cs.AIcited by 22

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Abstract (arXiv)

The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this paper, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, the sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. Along the way, key recommendations are made for responsible AER. The objective of the sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the sheet acts as a useful introductory document on emotion recognition (complementing survey articles).

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 23 of its 179 references that are in the map. First posted on arXiv 17 Sep 2021.

Builds on

23 of 179 references.

References · 23

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11 more
  1. 2020Debiasing Skin Lesion Datasets and Models? Not So Fastcited by 43
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 9; 2023: 8; 2024: 0; 2025: 4; 2026: 1.

22 papers.

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  1. 2023AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languagescited by 68
  2. 2022Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotationcited by 67
  3. 2022Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Predictioncited by 45
  4. 2023SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)cited by 41
  5. 2022A Multimodal Corpus for Emotion Recognition in Sarcasmcited by 29
  6. 2023A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognitioncited by 29
  7. 2022Best Practices in the Creation and Use of Emotion Lexiconscited by 27
  8. 2022Challenges in Applying Explainability Methods to Improve the Fairness of NLP Modelscited by 20
  9. 2022Deep Emotion Recognition in Textual Conversations: A Surveycited by 12
  10. 2022Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexiconscited by 6
  11. 2023Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversationscited by 3
  12. 2023LEIA: Linguistic Embeddings for the Identification of Affectcited by 3
10 more
  1. 2023GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapterscited by 2
  2. 2023Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across Agecited by 1
  3. 2025Targeted Distillation for Sentiment Analysiscited by 1
  4. 2022Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canadacited by 0
  5. 2022Computational Charisma -- A Brick by Brick Blueprint for Building Charismatic Artificial Intelligencecited by 0
  6. 2023Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification Using Graph Neural Networks?cited by 0
  7. 2025ELSA: A Style Aligned Dataset for Emotionally Intelligent Language Generationcited by 0
  8. 2025Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Modelscited by 0
  9. 2025An Architecture-Led Hybrid Report on Body Language Detection Projectcited by 0
  10. 2026Who Determines the Meaning of an Emotion? Affective Sovereignty as an Epistemic Consequence of Measurement Limitscited by 0