---
title: "Sarcastic or Not — Ghosh 2015"
description: "The authors recast sarcasm detection as a word sense disambiguation problem."
authors: ["Debanjan Ghosh", "Weiwei Guo", "Smaranda Muresan"]
year: 2015
venue: "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D15-1116/"
doi: "10.18653/v1/d15-1116"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ghosh-2015-sarcastic-or-not-word-embeddings/"
---
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# Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words

**Authors:** [Debanjan Ghosh](https://md.research.elamur.ai/free/en/authors/debanjan-ghosh/), [Weiwei Guo](https://md.research.elamur.ai/free/en/authors/weiwei-guo/), [Smaranda Muresan](https://md.research.elamur.ai/free/en/authors/smaranda-muresan/) · **Year:** 2015 · **Venue:** Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D15-1116/) · [PDF](https://aclanthology.org/D15-1116.pdf) · [DOI 10.18653/v1/d15-1116](https://doi.org/10.18653/v1/d15-1116) · [OpenAlex](https://openalex.org/W2250480277)

**Citations:** 116 citations (openalex-snapshot · 2026-10-14) · [25](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The authors recast sarcasm detection as a word sense disambiguation problem. Instead of labeling whole utterances, they ask whether a particular target word in a tweet carries its literal or its sarcastic sense. They refer to this formulation as LSSD, short for Literal/Sarcastic Sense Disambiguation.

To find words that can flip meaning, they had MTurk workers rewrite hashtag-labeled sarcastic tweets so that each rewrite stated what the author actually meant. They then aligned the originals with the rewrites using unsupervised methods (Barzilay & McKeown co-training, filtered with Giza++/Moses translation scores) to extract semantically opposite word pairs.

For each target word, they built training data from Twitter: sarcastic tweets marked with #sarcasm or #sarcastic, and literal tweets that were either unmarked or carried sentiment hashtags. They compared several distributional semantics approaches. An SVM with a modified kernel based on word embeddings performed best, beating a strong lexical SVM baseline by roughly 7–10% F1.

This matters because sentiment systems that miss sarcasm misread opinions. The released datasets support further work on the problem.

## Key points
- Crowdsourcing step: 1,000 sarcastic tweets, each rephrased by 5 Turkers, produced 5,000 SM-IM pairs. Co-training extracted 367 paraphrase pairs. Filtering at φ ≥ 0.8 plus manual checks left 80 opposite pairs, which gave 70 target words after lemmatization.
- Data collection: about 2.54M tweets were gathered via the Twitter API with an 80/10/10 split. Requiring at least 400 instances per sense left 37 usable targets, ranging from "love" (26,802) to "mature" (427).
- Best F1 was 97% for S vs. L but only 84% for S vs. L_sent. This confirms that literal tweets carrying sentiment are much harder to separate from sarcastic ones.
- Gains over the SVM baseline were 10% F1 (S vs. L) and 8% F1 (S vs. L_sent). Embedding methods held up well even on targets with little training data.
- Pre-trained GloVe vectors (2B tweets, 100 dimensions) averaged 88.9% F1 on S vs. L, about 7% below embeddings trained on the task's own tweets. Data is published at github.com/debanjanghosh/sarcasm_wsd.

*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: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/)

## Cited by

Cited by

25 papers of our library cite it (of the 140673 whose bibliographies we read); 15 of them have a page here, 10 not yet.

- [R^3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://md.research.elamur.ai/free/en/papers/chakrabarty-2020-r-textasciicircum3-reverse-retrieve-rank/)
- [Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media](https://md.research.elamur.ai/free/en/papers/dong-2020-transformer-based-context-aware-sarcasm/)
- [Linguistic Features in Automatic Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/ducret-2020-linguistic-features-automatic-sarcasm-detection/)
- [From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://md.research.elamur.ai/free/en/papers/farha-2020-arabic-sentiment-analysis-sarcasm-detection/)
- [Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic](https://md.research.elamur.ai/free/en/papers/farha-2021-overview-wanlp-2021-shared-task/)
- [Sarcasm Analysis Using Conversation Context](https://md.research.elamur.ai/free/en/papers/ghosh-2018-sarcasm-analysis-conversation-context/)
- [Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity](https://md.research.elamur.ai/free/en/papers/ghosh-2020-interpreting-verbal-irony-linguistic-strategies/)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://md.research.elamur.ai/free/en/papers/ghosh-2020-report-2020-sarcasm-detection-shared/)
- [Neural Sarcasm Detection using Conversation Context](https://md.research.elamur.ai/free/en/papers/jaiswal-2020-neural-sarcasm-detection-conversation-context/)
- [Are Word Embedding-based Features Useful for Sarcasm Detection?](https://md.research.elamur.ai/free/en/papers/joshi-2016-are-word-embedding-based-features/)
- [Automatic Sarcasm Detection: A Survey](https://md.research.elamur.ai/free/en/papers/joshi-2016-automatic-sarcasm-detection-survey/)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://md.research.elamur.ai/free/en/papers/liu-2022-dual-channel-framework-sarcasm-recognition/)
- [Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/pan-2020-modeling-intra-inter-modality-incongruity/)
- [A Transformer-based approach to Irony and Sarcasm detection](https://md.research.elamur.ai/free/en/papers/potamias-2019-transformer-based-approach-irony-sarcasm/)
- [Detecting Sarcasm in Multimodal Social Platforms](https://md.research.elamur.ai/free/en/papers/schifanella-2016-detecting-sarcasm-multimodal-social-platforms/)

## Cite

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

```bibtex
@inproceedings{ghosh2015sarcastic,
  title = {{Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words}},
  author = {Ghosh, Debanjan and Guo, Weiwei and Muresan, Smaranda},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
  year = {2015},
  doi = {10.18653/v1/d15-1116},
  url = {https://doi.org/10.18653/v1/d15-1116}
}
```

- [← previous Rule-based Coreference Resolution in German Historic Novels](https://md.research.elamur.ai/free/en/papers/krug-2015-rule-based-coreference-resolution-german/)
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