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GoEmotions: A Dataset of Fine-Grained Emotions

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arxiv 2005.00547 v2 pith:VJ5XRZEZ submitted 2020-05-01 cs.CL

Dorottya Demszky , Dana Movshovitz-Attias , Jeongwoo Ko , Alan Cowen , Gaurav Nemade , Sujith Ravi This is my paper · ORCID
classification cs.CL
keywords emotiondatasetfine-grainedgoemotionsachievesacrossadaptableadvancement
verification ladder T0 review T1 audit T2 compute T3 formal
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Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement.

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Forward citations

Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A new dataset and benchmark maps movie clips to distributions of audience emotional reactions derived from YouTube comments, showing that finetuned vision-language models can predict these distributions from video alone.

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    cs.CV 2025-12 conditional novelty 6.0 of 10

    An emotion-aware vision-language-action driving model estimates VAD emotion from commands and uses it to improve grounding and waypoint planning.

  3. Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Backtranslation and paraphrasing produce competitive or better classification gains than zero-shot and few-shot generation when augmenting a low-resource emotion dataset.

  4. Moodifier: MLLM-Enhanced Emotion-Driven Image Editing

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A training-free editing pipeline that uses a large emotion-annotated dataset and a fine-tuned CLIP model to modify only the parts of an image that convey a target emotion.

  5. Abstract Counterfactuals for Language Model Agents

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Counterfactuals for LM agents computed over a high-level abstraction of the action, instead of its tokens, preserve the observed action's meaning across counterfactual contexts far more often than token-level counterfactuals.

  6. SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    SocialMaze is a six-task benchmark that claims to evaluate LLM social reasoning along deep reasoning, dynamic interaction, and information uncertainty dimensions.

  7. MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset

    cs.AI 2025-08 conditional novelty 5.0 of 10

    A new VR-based emotion dataset with synchronized eye tracking, body motion, EMG, and GSR from 13 participants, evaluated with classifiers but with questionable validation.

  8. AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries

    cs.CL 2025-08 conditional novelty 5.0 of 10

    Eight LLMs show measurably different emotional tones in mental-health answers: anxiety prompts produced near-saturated fear scores, depression prompts the most sadness, and stress prompts the most optimism.

  9. MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning

    cs.AI 2025-05 conditional novelty 5.0 of 10

    MAPLE uses graph-influence scores to select and pseudo-label the most useful unlabeled examples, then adaptively chooses demonstrations per query, improving many-shot in-context learning with few human labels.

  10. Investigating Algorithmic Bias in YouTube Shorts

    cs.SI 2025-07 reject novelty 4.0 of 10

    YouTube Shorts recommendations from political seeds drift to entertainment and positive-emotion content within the first few steps, with the drift unchanged by simulated watch-time.

  11. Crowd-SFT: Crowdsourcing for LLM Alignment

    cs.HC 2025-06 conditional novelty 4.0 of 10

    A competitive multi-group fine-tuning framework with point rewards correlated to Shapley values reduced simulated model distance by up to 55% and tracked user contributions reasonably in vector-space experiments.

  12. The Super Emotion Dataset

    cs.CL 2025-05 reject novelty 4.0 of 10

    The paper presents SuperEmotion, an aggregated NLP dataset of roughly 555k samples (abstract says 519k) remapped to Shaver's six emotions plus neutral, with no experimental validation.

  13. Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME

    cs.CL 2025-06 reject novelty 2.0 of 10

    On the EmoNoBa Bangla emotion dataset, a boosted decision tree achieves 0.7860 macro F1, the best among classical models tested but below transformer-based baselines.

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