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PoETIC: A Re-framing of Context Dependent Emotion Detection

Paper · 2026 · Nirmal Surange and Manish Shrivastava

Summary

Most text-based emotion datasets label an utterance with emotions as if the feeling were fully contained in the words. The authors argue this is misleading. Without prosody, faces or emojis, written emotion depends heavily on the situation. Annotators labelling isolated sentences are therefore implicitly imagining a context. The paper recasts the task at the level of utterance–emotion pairs. Each pair is assigned one of three classes:

  • Emotion-Obvious: the text conveys the emotion on its own.
  • Emotion-Plausible: it could convey the emotion in some imagined situation.
  • Emotion-Implausible: no context would make it fit.

They release PoETIC, a human-annotated benchmark built from filtered GoEmotions test sentences across six Ekman emotions plus Neutral. When asked to imagine contexts, annotators accept far more emotions per sentence than the original gold labels contain. The original GoEmotions tags also do not line up well with these plausibility judgments. Zero- and few-shot baselines with large and small language models are provided. The resource matters because it exposes how ambiguous single-label emotion annotation is. It also offers a way to test whether models reason about implicit context.

Key points

  • PoETIC contains about 7k utterance–emotion pairs: 1,000 sentences × 7 labels (Fear, Anger, Sadness, Joy, Disgust, Surprise, Neutral). It is released on GitHub (nirmalsurange/PoETIC).
  • The sentences come from the GoEmotions test split. Filtering reduced its 5,427 sentences to 4,770 clean ones, from which 1,000 were selected. The removals were:
    • 268 sentences containing emojis
    • 267 with foul language
    • 106 short or noisy items
    • 16 with hashtags or URLs
  • Annotators judged an average of 4.55 emotions per utterance as possible, versus 1.09 gold emotions per utterance in the GoEmotions test set.
  • The three labels are abbreviated EO, EP and EI. For Neutral, the authors add a constraint: no sentence may receive the Emotion-Obvious label for Neutral and for some other emotion at the same time.
  • Baselines cover three LLMs (GPT-5-mini, Gemini 2.5 Flash, Llama3-70B via Groq) and two small language models, each tested in zero-shot and few-shot settings.

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

 Institutions

🎓International Institute of Information Technology, HyderabadHyderabad · India38 works · 1,821 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 worksSocial Psychology · 306 works

🧭 Topics 1Research subjects and languages the work is about.Sentiment & emotion · 972 works

Cite

Cite as Nirmal Surange and Manish Shrivastava. 2026. PoETIC: A Re-framing of Context Dependent Emotion Detection. Proceedings of Computational Affective Science (CAS) @ LREC 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4f24agmr53nm

@inproceedings{surange2026poetic,
  title = {{PoETIC: A Re-framing of Context Dependent Emotion Detection}},
  author = {Nirmal Surange and Manish Shrivastava},
  booktitle = {Proceedings of Computational Affective Science (CAS) @ LREC 2026},
  year = {2026},
  doi = {10.63317/4f24agmr53nm},
  url = {https://doi.org/10.63317/4f24agmr53nm}
}

References

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