---
title: "Lancaster at SemEval-2018 Task 3: Investigating Ironic Features"
description: "The paper describes Lancaster's entry to SemEval-2018 Task 3, which asks systems to label English tweets as ironic or not. 10 references."
authors: ["Edward Dearden", "Alistair Baron"]
year: 2018
venue: "Proceedings of the 12th International Workshop on Semantic Evaluation"
source: "https://aclanthology.org/S18-1096/"
doi: "10.18653/v1/s18-1096"
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/dearden-2018-lancaster-semeval-2018-task-3/"
---
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# Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets

**Authors:** Edward Dearden and [Alistair Baron](https://md.research.elamur.ai/free/en/authors/alistair-baron/) · **Year:** 2018 · **Venue:** [Proceedings of the 12th International Workshop on Semantic Evaluation](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2018](https://md.research.elamur.ai/free/en/venues/semeval/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/S18-1096/) · [PDF](https://aclanthology.org/S18-1096.pdf) · [DOI 10.18653/v1/s18-1096](https://doi.org/10.18653/v1/s18-1096) · [OpenAlex](https://openalex.org/W2807071089)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
The paper describes Lancaster's entry to SemEval-2018 Task 3, which asks systems to label English tweets as ironic or not. Using only the tweet text from the organisers' annotated data, the authors built a hand-designed, explainable feature set and fed it to an untuned LinearSVC from scikit-learn. The features fall into four groups: tweet-surface cues (punctuation, emoji, elongated words, capitalisation), sentiment cues (VaderSentiment scores, emoji sentiment, polarity shifts between words), complexity/style measures (readability, WordNet ambiguity, lexical density), and large data-driven Bag-of-X sets. The submitted combination scored F1 0.5914, beating a bag-of-words baseline. Surface tweet features alone did best. The authors then ablated by irony subtype. The classifier mainly relies on polarity contrast and almost fails once such tweets are removed. The work matters less for its score than for its diagnosis: lexical features add little, and situational or non-contrastive irony likely needs contextual and world knowledge.

## Key points
- Feature-set results (F1): Baseline 0.5516, Tweet 0.6046 (highest), Bag-of-X 0.5522, Sentiment 0.5007, Complexity 0.5125, Submission 0.5914, Reduced 0.5901, Combined 0.5889.
- Feature set sizes:
  - Submission (Tweet + Sentiment + Complexity): 126 features.
  - Reduced (emoji and punctuation token features dropped): 27 features.
  - Combined: 3,537 features.
- Tools used:
  - NLTK Tweet Tokeniser and NLTK POS tagger.
  - USAS semantic tagger for Bag-of-X semantic tags.
  - VaderSentiment for sentiment scores.
  - Kralj Novak et al. emoji sentiment lexicon.
  - WordNet sense counts for ambiguity.
  - A random forest for comparison.
- Recall on ironic tweets differed by subtype: 82% for polarity-contrast irony versus 50% for other kinds.
- In the retraining experiment, polarity-contrast tweets were removed from both training and test data. The classifier then caught only 4 of 147 ironic test tweets.
- Character elongation and positive words such as "love", "great" and "fun" carried heavy weight toward the ironic class.

## Interesting ideas
- Represent a sentence as a sequence of positive/neutral/negative words and count the flips from positive to negative, so that "love … awful" yields a measurable clash signal that can be explained.
- Break the evaluation down by irony subtype, then retrain with the easy subtype removed, to reveal whether a model has learned irony itself or just one shortcut.
- Choose targeted, predefined features over n-grams so that the linear model's top weights can be read and justified directly.
- Collapse repeated punctuation such as "..." into a single token, and keep elongated spellings ("Greeeeat") as a separate emphasis count instead of discarding them as noise.
- Many ironic tweets describe a negative situation without negative words (going to the dentist), so polarity must come from knowledge about the concept rather than from the lexicon.

## For Mova

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

- **I1** (frames): crate prag could compute polarity flips along UD-linked arguments, such as a positive governing verb with a negative complement, as one explicit, traceable cue for its irony judgement.
- **I2** (learn): this is a good negative-control pattern; removing the dominant easy class from training and test data shows whether a learned rule truly generalises.
- **I3** (learn): inspecting the weights of the perceptron or repair-rule learner in the same way keeps every learned decision explainable, consistent with Mova's no-bag-of-words stance.
- **I4** (tokens): crate en could normalise the elongated form to its lemma while attaching an emphasis attribute, which later stages can use as an irony or intensity cue.
- **I5** (world): attaching default evaluative associations to events and entities in the knowledge layer would let prag spot a clash between stated delight and an implicitly unpleasant event.

*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

- [Lancaster University](https://md.research.elamur.ai/free/en/universities/lancaster-university/) — University, Lancaster, United Kingdom · 16 works · 1,153 citations

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🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 1,373 works · [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 608 works

## Cite

Cite as: Edward Dearden and Alistair Baron. 2018. Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets. Proceedings of the 12th International Workshop on Semantic Evaluation.

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

```bibtex
@inproceedings{dearden2018lancaster,
  title = {{Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets}},
  author = {Edward Dearden and Alistair Baron},
  booktitle = {Proceedings of the 12th International Workshop on Semantic Evaluation},
  year = {2018},
  doi = {10.18653/v1/s18-1096},
  url = {https://doi.org/10.18653/v1/s18-1096}
}
```

- [← previous KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection](https://md.research.elamur.ai/free/en/papers/durlich-2018-kluenicorn-semeval-2018-task-3/)
- [next → PunFields at SemEval-2018 Task 3: Detecting Irony by Tools of Humor Analysis](https://md.research.elamur.ai/free/en/papers/mikhalkova-2018-punfields-semeval-2018-task-3/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/6/)

## References

10 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Antonio Reyes, Paolo Rosso, and Davide Buscaldi. 2012. From humor recognition to irony detection: The figurative language of social media. Data Knowl. Eng., 74:1–12. [↗](https://doi.org/10.1016/j.datak.2012.02.005)
2. David Bamman and Noah A Smith. 2015. Contextualized sarcasm detection on twitter. In ICWSM, pages 574–577. [find](https://scholar.google.com/scholar?q=David+Bamman+and+Noah+A+Smith%2E+2015%2E+Contextualized+sarcasm+detection+on+twitter%2E+In+ICWSM%2C+pages+574%E2%80%93577%2E)
3. Oren Tsur, Dmitry Davidov, and Ari Rappoport. 2010. Icwsm-a great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. [find](https://scholar.google.com/scholar?q=Oren+Tsur%2C+Dmitry+Davidov%2C+and+Ari+Rappoport%2E+2010%2E+Icwsm%2Da+great+catchy+name%3A+Semi%2Dsupervised+recognition+of+sarcastic+sentences+in+online+product+reviews%2E)
4. Francesco Barbieri, Horacio Saggion, and Francesco Ronzano. 2014. [Modelling sarcasm in twitter, a novel approach](https://md.research.elamur.ai/free/en/papers/barbieri-2014-modelling-sarcasm-twitter-novel-approach/). In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 50–58.
5. Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2016a. [Exploring the realization of irony in twitter data](https://md.research.elamur.ai/free/en/papers/hee-2016-exploring-realization-irony-twitter-data/). In LREC.
6. Steven Bird and Edward Loper. 2004. [Nltk: the natural language toolkit](https://md.research.elamur.ai/free/en/papers/bird-2006-nltk-natural-language-toolkit/). In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions, page 31. Association for Computational Linguistics.
7. Cynthia Van Hee, Els Lefever, and Veronique Hoste. 2016b. [Monday mornings are my fave : #not exploring the automatic recognition of irony in english tweets](https://md.research.elamur.ai/free/en/papers/hee-2016-monday-mornings-are-my-fave/). In Proceedings of COLING 2016, 26th International Conference on Computational Linguistics, pages 2730–2739. ACL.
8. Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010. Semi-supervised recognition of sarcastic sentences Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018. [SemEval-2018 Task 3: Irony Detection in English Tweets](https://md.research.elamur.ai/free/en/papers/hee-2018-semeval-2018-task-3-irony/). In Proceedings of the 12th International Workshop on Semantic Evaluation, SemEval-2018, New Orleans, LA, USA. Association for Computational Linguistics.
9. Byron C. Wallace. 2015. Computational irony: A survey and new perspectives. Artif. Intell. Rev., 43(4):467–483. [find](https://scholar.google.com/scholar?q=Byron+C%2E+Wallace%2E+2015%2E+Computational+irony%3A+A+survey+and+new+perspectives%2E+Artif%2E+Intell%2E+Rev%2E%2C+43%284%29%3A467%E2%80%93483%2E)
10. Zelin Wang, Zhijian Wu, Ruimin Wang, and Yafeng Ren. 2015. Twitter sarcasm detection exploiting a context-based model. In Proceedings, Part I, of the 16th International Conference on Web Information Systems Engineering — WISE 2015 - Volume 9418, pages 77–91, New York, NY, USA. Springer-Verlag New York, Inc. [find](https://scholar.google.com/scholar?q=Zelin+Wang%2C+Zhijian+Wu%2C+Ruimin+Wang%2C+and+Yafeng+Ren%2E+2015%2E+Twitter+sarcasm+detection+exploiting+a+context%2Dbased+model%2E+In+Proceedings%2C+Part+I%2C+of+the+16th+International+Conference+on+Web+Information+S)

*Updated 24 October 2026*