---
title: "EmoNet: Fine-Grained Emotion Detection — Abdul-Mageed 2017"
description: "The paper tackles automatic emotion recognition in text, where progress has been held back by the lack of large labeled datasets."
authors: ["Muhammad Abdul-Mageed", "Lyle Ungar"]
year: 2017
venue: "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P17-1067/"
doi: "10.18653/v1/p17-1067"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mageed-2017-emonet-fine-grained-emotion-detection/"
---
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# EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks

**Authors:** [Muhammad Abdul-Mageed](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/), [Lyle Ungar](https://md.research.elamur.ai/free/en/authors/lyle-ungar/) · **Year:** 2017 · **Venue:** Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P17-1067/) · [PDF](https://aclanthology.org/P17-1067.pdf) · [DOI 10.18653/v1/p17-1067](https://doi.org/10.18653/v1/p17-1067) · [OpenAlex](https://openalex.org/W2741447225)

**Citations:** 378 citations (openalex-snapshot · 2026-10-14) · [108](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper tackles automatic emotion recognition in text, where progress has been held back by the lack of large labeled datasets. The authors avoid manual annotation by using distant supervision. They gathered tweets carrying emotion hashtags that correspond to the 24 emotion categories in Robert Plutchik's psychological model. These categories form three concentric circles of intensity on his "wheel." Starting from more than a quarter of a billion tweets, a strict cleaning pipeline removed duplicates and ambiguous posts. Only tweets of five or more words with the emotion hashtag at the end were kept. A human annotation study confirmed that the hashtag labels are largely reliable. Gated recurrent neural networks (GRNNs) trained on this data clearly outperform traditional online classifiers and set a new state-of-the-art across the 24 emotions. The authors also merge intensity levels into Plutchik's 8 primary dimensions and report very high accuracy on that task, which they believe had not been modeled before. The work shows that large, theory-grounded, automatically labeled social media data can support deep emotion models.

## Key points
- The dataset covers the 24 Plutchik emotions. Each circle (plutchik-1, plutchik-2, plutchik-3) is treated as an 8-way classification task with an 80/10/10 TRAIN/DEV/TEST split. Human judgment put the accuracy of the hashtag labels at about 90%.
- GRNNs reach an average accuracy of 87.58% over the 24 emotion types. F-scores are 91.21% (plutchik-1), 82.32% (plutchik-2) and 87.47% (plutchik-3), roughly 25–29 points above the Passive Aggressive (PAC) baseline.
- When intensity levels are collapsed into the 8 primary dimensions (e.g., serenity/joy/ecstasy → joy), the model reaches 95.68% accuracy.
- On the overlapping basic emotions, the system is 4.53% more accurate than Volkova and Bachrach (2016). On six shared emotions, their pre-trained model scores 26.95% on this test set, compared with 80.12% for EmoNet.
- Two attempts to augment the training data did not help: adding tweets from neighboring intensity levels, and adding tweets with the hashtag in the last quarter of the text. The authors take this as evidence that strict data filtering matters.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

108 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 103 not yet.

- [Modeling Empathy and Distress in Reaction to News Stories](https://md.research.elamur.ai/free/en/papers/buechel-2018-modeling-empathy-distress-reaction-news/)
- [The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse](https://md.research.elamur.ai/free/en/papers/cabot-2020-pragmatics-behind-politics-modelling-metaphor/)
- [SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text](https://md.research.elamur.ai/free/en/papers/chatterjee-2019-semeval-2019-task-3-emocontext/)
- [CARER: Contextualized Affect Representations for Emotion Recognition](https://md.research.elamur.ai/free/en/papers/saravia-2018-carer-contextualized-affect-representations-emotion/)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://md.research.elamur.ai/free/en/papers/zhong-2019-knowledge-enriched-transformer-emotion-detection/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{abdulmageed2017emonet,
  title = {{EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks}},
  author = {Abdul-Mageed, Muhammad and Ungar, Lyle},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2017},
  doi = {10.18653/v1/p17-1067},
  url = {https://doi.org/10.18653/v1/p17-1067}
}
```

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