---
title: "EmoWordNet — paper by Badaro et al. (2018)"
description: "Emotion lexicons for English are much smaller than sentiment lexicons. 55 references, cited by 4 papers here."
authors: ["Gilbert Badaro", "Hussein Jundi", "Hazem Hajj", "Wassim El-Hajj"]
year: 2018
venue: "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics"
source: "https://aclanthology.org/S18-2009/"
doi: "10.18653/v1/s18-2009"
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/badaro-2018-emowordnet-automatic-expansion-emotion-lexicon/"
---
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# EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet

**Authors:** Gilbert Badaro, Hussein Jundi, [Hazem Hajj](https://md.research.elamur.ai/free/en/authors/hazem-hajj/) and [Wassim El-Hajj](https://md.research.elamur.ai/free/en/authors/wassim-el-hajj/) · **Year:** 2018 · **Venue:** [Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics](https://md.research.elamur.ai/free/en/venues/starsem/) ([\*SEM 2018](https://md.research.elamur.ai/free/en/venues/starsem/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/S18-2009/) · [PDF](https://aclanthology.org/S18-2009.pdf) · [DOI 10.18653/v1/s18-2009](https://doi.org/10.18653/v1/s18-2009) · [OpenAlex](https://openalex.org/W2806028205)

**Citations:** 39 citations (OpenAlex, 2026-10-14) · 4 in our library, of the 141,840 papers whose bibliographies we read

## Summary
Emotion lexicons for English are much smaller than sentiment lexicons. For example, DepecheMood has about 37K entries, while SentiWordNet has about 150K. The authors grow DepecheMood into a larger resource called EmoWordNet without any manual annotation. Because DepecheMood lemmas are already aligned with English WordNet (EWN), they pass emotion scores across synsets using synonymy alone:

- A synset takes the average score of its DepecheMood members.
- Unscored members of that synset then inherit the synset's score.
- Newly reached synsets are scored from those members.
- The loop repeats until no new items appear and the scores stabilise.

The result covers 67,738 terms and roughly 60K synsets, each scored on DepecheMood's 8 emotion dimensions. That is about 1.8 times the original size. The authors test it without supervision on the SemEval 2007 Affective Text headlines, for both regression and classification. EmoWordNet beats DepecheMood on both tasks, and the gain is largest in classification. This matters because it shows a cheap, reproducible way to widen an affect lexicon using an existing ontology. The resource is publicly released.

## Key points
- **Seed lexicon:** the normalized-score variant of DepecheMood, with 37,771 lemma+POS entries.
  - Its 8 labels are afraid, amused, angry, annoyed, don't care, happy, inspired and sad.
- **Expansion size:** 29,967 new EWN terms, giving 67,738 terms in total.
  - The synset count is 59,952 in the method section but "58K" in the conclusion, which is an internal inconsistency.
- **Evaluation setup:** SemEval 2007 Affective Text, with 1,000 headlines.
  - Label mapping: Fear→Afraid, Anger→Angry, Joy→Happy, Sadness→Sad, Surprise→Inspired. Disgust was dropped.
  - Headlines were lemmatized with the NLTK WordNet lemmatizer, and n-grams up to 3 were checked for multi-word entries.
- **Results:**
  - Token coverage rose only slightly, from 67.1% to 68.6%.
  - Average Pearson correlation rose from 0.40 to 0.46.
  - Average F1 rose from 0.24 to 0.40. Joy jumped from 0.16 to 0.48, and Anger moved from 0.00 to 0.17.
- **Feature design and errors:**
  - Averaging term scores per headline worked better than summing them, for both lexicons.
  - Headlines of 5–10 terms were classified best.
  - Some gold labels contradict each other (joy and sadness on the same headline).

## Interesting ideas
- Spread a lexical property through WordNet with a fixpoint loop (lemma→synset average→unscored lemma→new synset) until scores stop changing; this is simple, deterministic and traceable.
- Choose the WordNet relation according to the property being transferred: synonymy keeps emotion intact, so it needs no manual check, while other relations might flip or blur it.
- When two label schemes do not match, write an explicit mapping table and drop labels that have no counterpart (Disgust) rather than forcing them.
- Contradictory gold emotions on one headline come from readers' differing viewpoints, so emotion is not a fixed property of the text.
- Averaging lexicon scores over content words, and noting that very short or very long inputs fail, gives a length-robust baseline with a known failure profile.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): Compiled affect or evaluative-polarity values in the WordNet ontology could be filled this way, with each value storing the seed lemma and synset path it came from, so every inherited value stays explainable.
- **I2** (world): When deriving attributes from WordNet, keep a per-property whitelist of safe relations (e.g. hypernymy for type, synonymy only for affect) instead of one generic propagation rule.
- **I3** (learn): Evaluations across different tag sets or UD dialects can use declared mappings plus an excluded-labels list, so scores are honest and comparable.
- **I4** (frames): Emotion or attitude frames could carry an explicit experiencer slot (author, character, reader), letting conflicting readings coexist rather than counting as errors.
- **I5** (answer): A lexicon-averaging baseline could serve as a cheap negative control for affect questions on fables, with input length logged as a diagnostic.

*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

- [American University of Beirut](https://md.research.elamur.ai/free/en/universities/american-university-of-beirut/) — University, Beirut, Lebanon · 17 works · 1,847 citations

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## Cite

Cite as: Gilbert Badaro, Hussein Jundi, Hazem Hajj and Wassim El-Hajj. 2018. EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet. Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics.

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

```bibtex
@inproceedings{badaro2018emowordnet,
  title = {{EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet}},
  author = {Gilbert Badaro, Hussein Jundi, Hazem Hajj and Wassim El-Hajj},
  booktitle = {Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics},
  year = {2018},
  doi = {10.18653/v1/s18-2009},
  url = {https://doi.org/10.18653/v1/s18-2009}
}
```

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## References

55 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*