--- title: "Sarcasm Detection on Czech and English Twitter — Ptáček 2014" description: "This COLING 2014 paper studies automatic sarcasm detection on Twitter in English and in Czech." authors: ["Tomáš Ptáček", "Ivan Habernal", "Jun Hong"] year: 2014 venue: "Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers" source: "https://aclanthology.org/C14-1022/" 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/ptacek-2014-sarcasm-detection-czech-english-twitter/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ptacek-2014-sarcasm-detection-czech-english-twitter/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) / Sarcasm Detection on Czech and English Twitter # Sarcasm Detection on Czech and English Twitter **Authors:** Tomáš Ptáček, [Ivan Habernal](https://md.research.elamur.ai/free/en/authors/ivan-habernal/), Jun Hong · **Year:** 2014 · **Venue:** Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers · **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/C14-1022/) · [PDF](https://aclanthology.org/C14-1022.pdf) · [OpenAlex](https://openalex.org/W2251210340) **Citations:** 168 citations (openalex-snapshot · 2026-10-14) · [62](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This COLING 2014 paper studies automatic sarcasm detection on Twitter in English and in Czech. It is the first work on sarcasm in Czech, and the authors know of none in any other Slavic language. Sarcasm can flip the apparent polarity of a message, so it often causes errors in sentiment analysis. Czech also has no language-specific lexical resources for this task. The authors therefore used supervised learning with features that do not depend on the language: word n-grams, character n-grams, skip-bigrams, patterns built from high-frequency and content words, pointedness, emoticons, punctuation, and POS-based cues. They tested MaxEnt and SVM classifiers with 5-fold cross-validation and compared several preprocessing pipelines to deal with Czech's rich morphology. They also built and released a manually annotated Czech tweet corpus. On English data, the approach clearly beat baselines adapted from earlier work. Czech results were much lower and serve as a first baseline. A control experiment suggests the gap comes partly from the small amount of training data and partly from the difficulty of the language itself. The work is a useful reference for sarcasm detection in other morphologically rich, low-resource languages. ## Key points - **Resources:** a Czech Twitter corpus of 7,000 manually labeled tweets, only 325 of them sarcastic, with inter-annotator agreement κ = 0.54. There are also two English sets of 100,000 tweets each, one balanced and one imbalanced, labeled using the #sarcasm hashtag. The data is released under CC BY-NC-SA and the code under GPL at liks.fav.zcu.cz/sarcasm. - **English results:** the best macro F-measure was 0.947 on the balanced set and 0.924 on the imbalanced set. MaxEnt generally did better than SVM, and the strongest feature sets combined character n-grams, pointedness, patterns and POS features. - **Czech result:** the best score was F-measure 0.582, from SVM with a feature set enriched by patterns. - **Effect of data size:** when the English data was cut down to the same size and class balance as the Czech data, F-measure fell to about 0.73. That shows data volume matters a lot, but English still scored well above Czech. - **Tooling:** experiments used the Brainy Java ML framework with a linear-kernel SVM. Naive Bayes and delta TF-IDF features were also tried but did not do as well. *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/) ## Cited by Cited by 62 papers of our library cite it (of the 140673 whose bibliographies we read); 25 of them have a page here, 37 not yet. - [Affective and Contextual Embedding for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/babanejad-2020-affective-contextual-embedding-sarcasm-detection/) - [Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model](https://md.research.elamur.ai/free/en/papers/cai-2019-multi-modal-sarcasm-detection-twitter/) - [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/) - [Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/farha-2022-sarcasm-detection-is-way-too/) - [SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic](https://md.research.elamur.ai/free/en/papers/farha-2022-semeval-2022-task-6-isarcasmeval/) - [Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal](https://md.research.elamur.ai/free/en/papers/ghosh-2017-magnets-sarcasm-making-sarcasm-detection/) - [Sarcasm Analysis Using Conversation Context](https://md.research.elamur.ai/free/en/papers/ghosh-2018-sarcasm-analysis-conversation-context/) - [SemEval-2018 Task 3: Irony Detection in English Tweets](https://md.research.elamur.ai/free/en/papers/hee-2018-semeval-2018-task-3-irony/) - [Deep contextualized word representations for detecting sarcasm and irony](https://md.research.elamur.ai/free/en/papers/ilic-2018-deep-contextualized-word-representations-detecting/) - [Generalizable Sarcasm Detection is Just Around the Corner, of Course!](https://md.research.elamur.ai/free/en/papers/jang-2024-generalizable-sarcasm-detection-is-just/) - [Automatic Sarcasm Detection: A Survey](https://md.research.elamur.ai/free/en/papers/joshi-2016-automatic-sarcasm-detection-survey/) - [JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams](https://md.research.elamur.ai/free/en/papers/kerner-2022-jct-semeval-2022-task-6/) - [A Large Self-Annotated Corpus for Sarcasm](https://md.research.elamur.ai/free/en/papers/khodak-2018-large-self-annotated-corpus-sarcasm/) - [Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context](https://md.research.elamur.ai/free/en/papers/lee-2020-augmenting-data-sarcasm-detection-unlabeled/) - [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/) - [AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques](https://md.research.elamur.ai/free/en/papers/lotfy-2022-alexu-al-semeval-2022-task/) - [Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction](https://md.research.elamur.ai/free/en/papers/min-2023-just-like-human-would-direct/) - [Exploring Author Context for Detecting Intended vs Perceived Sarcasm](https://md.research.elamur.ai/free/en/papers/oprea-2019-exploring-author-context-detecting-intended/) - [iSarcasm: A Dataset of Intended Sarcasm](https://md.research.elamur.ai/free/en/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/) - [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/poria-2016-deeper-look-into-sarcastic-tweets/) - [WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony](https://md.research.elamur.ai/free/en/papers/rohanian-2018-wlv-semeval-2018-task-3/) - [Reactive Supervision: A New Method for Collecting Sarcasm Data](https://md.research.elamur.ai/free/en/papers/shmueli-2020-reactive-supervision-new-method-collecting/) - [Reasoning with Sarcasm by Reading In-Between](https://md.research.elamur.ai/free/en/papers/tay-2018-reasoning-sarcasm-reading-between/) - [Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data](https://md.research.elamur.ai/free/en/papers/wang-2020-building-bridge-method-image-text/) - [Tweet Sarcasm Detection Using Deep Neural Network](https://md.research.elamur.ai/free/en/papers/zhang-2016-tweet-sarcasm-detection-deep-neural/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{ptacek2014sarcasm, title = {{Sarcasm Detection on Czech and English Twitter}}, author = {Ptáček, Tomáš and Habernal, Ivan and Hong, Jun}, booktitle = {Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers}, year = {2014}, url = {https://aclanthology.org/C14-1022/} } ``` - [← previous Results of the WMT14 Metrics Shared Task](https://md.research.elamur.ai/free/en/papers/machacek-2014-results-wmt14-metrics-shared-task/) - [next → Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation](https://md.research.elamur.ai/free/en/papers/pradhan-2014-scoring-coreference-partitions-predicted-mentions/) - [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) ## References 7 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Julie Kane Ahkter and Steven Soria. 2010. Sentiment analysis: Facebook status messages. Technical report, Stanford University. Final Project CS224N. [find](https://scholar.google.com/scholar?q=Julie+Kane+Ahkter+and+Steven+Soria%2E+2010%2E+Sentiment+analysis%3A+Facebook+status+messages%2E+Technical+report%2C+Stanford+University%2E+Final+Project+CS224N%2E) 2. Joshua M Averbeck and Dale Hample. 2008. Ironic message production: How and why we produce ironic messages. Communication Monographs, 75(4):396–410. [↗](https://doi.org/10.1080/03637750802512389) 3. Joshua M Averbeck. 2013. Comparisons of ironic and sarcastic arguments in terms of appropriateness and effectiveness in personal relationships. Argumentation & Advocacy, 50(1). [↗](https://doi.org/10.1080/00028533.2013.11821809) 4. Daniel M Bikel, Scott Miller, Richard Schwartz, and Ralph Weischedel. 1997. Nymble: a high-performance learning name-finder. In Proceedings of the fifth conference on Applied natural language processing, pages 194–201. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.cmp-lg/9803003) [our card](https://md.research.elamur.ai/free/en/papers/bikel-1997-nymble-high-performance-learning-name/) 5. Ben Blamey, Tom Crick, and Giles Oatley. 2012. R U : -) or : -( ? character- vs. word-gram feature selection for sentiment classification of OSN corpora. In Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pages 207–212. Springer. [find](https://scholar.google.com/scholar?q=Ben+Blamey%2C+Tom+Crick%2C+and+Giles+Oatley%2E+2012%2E+R+U+%3A+%2D%29+or+%3A+%2D%28+%3F+character%2D+vs%2E+word%2Dgram+feature+selection+for+sentiment+classification+of+OSN+corpora%2E+In+Proceedings+of+AI%2D2012%2C+The+Thirty%2Dsecond+S) 6. Cristina Bosco, Viviana Patti, and Andrea Bolioli. 2013. Developing corpora for sentiment analysis: The case of irony and senti-tut. IEEE Intelligent Systems, 28(2):55–63. [↗](https://doi.org/10.1109/mis.2013.28) 7. Tomáš Brychcı́n and Ivan Habernal. 2013. Unsupervised improving of sentiment analysis using global target context. In Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013, pages 122–128, Hissar, Bulgaria, September. INCOMA Ltd. Shoumen, BULGARIA. [find](https://scholar.google.com/scholar?q=Toma%CC%81s%CC%8C+Brychc%C4%B1%CC%81n+and+Ivan+Habernal%2E+2013%2E+Unsupervised+improving+of+sentiment+analysis+using+global+target+context%2E+In+Proceedings+of+the+International+Conference+Recent+Advances+in+Natural+Language+)