--- title: "Harnessing Context Incongruity for Sarcasm — Joshi 2015" description: "Linguists have long argued that sarcasm comes from a mismatch between what is said and what the context leads us to expect." authors: ["Aditya Joshi", "Vinita Sharma", "Pushpak Bhattacharyya"] year: 2015 venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)" source: "https://aclanthology.org/P15-2124/" doi: "10.3115/v1/p15-2124" 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/joshi-2015-harnessing-context-incongruity-sarcasm-detection/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/joshi-2015-harnessing-context-incongruity-sarcasm-detection/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2015](https://md.research.elamur.ai/free/en/papers/2015/) / Harnessing Context Incongruity for Sarcasm Detection # Harnessing Context Incongruity for Sarcasm Detection **Authors:** [Aditya Joshi](https://md.research.elamur.ai/free/en/authors/aditya-joshi/), Vinita Sharma, [Pushpak Bhattacharyya](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) · **Year:** 2015 · **Venue:** Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short 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/P15-2124/) · [PDF](https://aclanthology.org/P15-2124.pdf) · [DOI 10.3115/v1/p15-2124](https://doi.org/10.3115/v1/p15-2124) · [OpenAlex](https://openalex.org/W2251920663) **Citations:** 322 citations (openalex-snapshot · 2026-10-14) · [93](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary Linguists have long argued that sarcasm comes from a mismatch between what is said and what the context leads us to expect. This mismatch is called context incongruity. Joshi, Sharma and Bhattacharyya turn this idea into features for a supervised sarcasm classifier. They separate two cases. In explicit incongruity, words of opposite sentiment polarity appear together. In implicit incongruity, a polar word conflicts with a phrase whose sentiment is only implied, such as an unpleasant situation. The explicit features come from earlier work on detecting thwarted expectations. The implicit features are sentiment-bearing verb and noun phrases, mined with an extended version of the Riloff et al. (2013) bootstrapping algorithm. The authors add these to unigram and pragmatic cues in an SVM. They test it on two tweet collections and a sample of the Internet Argument Corpus. The new features raise F-score over a unigram baseline for both tweets and forum posts, and the system beats two published systems by 10–20 F-score points. The paper bases sarcasm detection on linguistic theory and makes an early attempt to use the previous post in a thread as context. ## Key points - **Features:** there are four groups, fed to LibSVM with an RBF kernel and evaluated with 5-fold cross-validation: - lexical unigrams, chosen with χ² and Categorical Proportional Difference; - pragmatic cues: emoticons, laughter, punctuation and capitals; - implicit phrases: 79 verb phrases and 202 noun phrases mined from about 4,000 tweets; - five explicit features: polarity flips, longest run of same-polarity words, counts of positive and negative words, and Lingpipe lexical polarity. - **Data:** - Tweet-A: 5,208 tweets, 4,170 sarcastic, labelled by hashtags. - Tweet-B: 2,278 tweets, 506 sarcastic, the manually labelled set from Riloff et al. - Discussion-A: 1,502 posts from the Internet Argument Corpus, 752 sarcastic. - **Results on Tweet-A and Discussion-A:** - On Tweet-A, all features together reach F = 0.8876. The unigram baseline scores 0.842, and the authors' reimplementation of the rule-based Riloff method scores at most 0.474. - On Discussion-A, F rises from 0.568 to 0.640. - **Comparison on Tweet-B:** the system reaches F = 0.61. Riloff et al. reported 0.51, and the authors' reimplementation of Maynard and Greenwood (2014) reaches 0.41. - **Prevalence and thread context:** - Only about 11% of 18,141 sarcastic tweets contain explicit incongruity, so most sarcasm is implicit. - Adding the preceding "elicitor" post to forum posts raises precision to 0.705 but lowers recall to 0.274, because only 15% of posts have an elicitor. *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 93 papers of our library cite it (of the 140884 whose bibliographies we read); 51 of them have a page here, 42 not yet. - [Affective and Contextual Embedding for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/babanejad-2020-affective-contextual-embedding-sarcasm-detection/) - [ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets](https://md.research.elamur.ai/free/en/papers/bashmal-2021-arsarcasm-shared-task-ensemble-bert/) - [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://md.research.elamur.ai/free/en/papers/castro-2019-towards-multimodal-sarcasm-detection-obviously/) - [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/) - [Multilingual Irony Detection with Dependency Syntax and Neural Models](https://md.research.elamur.ai/free/en/papers/cignarella-2020-multilingual-irony-detection-dependency-syntax/) - [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/) - [“When Numbers Matter!”: Detecting Sarcasm in Numerical Portions of Text](https://md.research.elamur.ai/free/en/papers/dubey-2019-when-numbers-matter-detecting-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 and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based Features](https://md.research.elamur.ai/free/en/papers/gaanoun-2021-sarcasm-sentiment-detection-arabic-language/) - [Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words](https://md.research.elamur.ai/free/en/papers/ghosh-2015-sarcastic-or-not-word-embeddings/) - [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/) - [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/) - [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://md.research.elamur.ai/free/en/papers/hazarika-2018-cascade-contextual-sarcasm-detection-online/) - [We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter](https://md.research.elamur.ai/free/en/papers/hee-2018-we-usually-don-t-like/) - [Deep contextualized word representations for detecting sarcasm and irony](https://md.research.elamur.ai/free/en/papers/ilic-2018-deep-contextualized-word-representations-detecting/) - [Neural Sarcasm Detection using Conversation Context](https://md.research.elamur.ai/free/en/papers/jaiswal-2020-neural-sarcasm-detection-conversation-context/) - [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/) - [Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation](https://md.research.elamur.ai/free/en/papers/jing-2023-multi-source-semantic-graph-based/) - [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/) - [How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature](https://md.research.elamur.ai/free/en/papers/joshi-2016-how-challenging-is-sarcasm-versus/) - [Sarcasm Target Identification: Dataset and An Introductory Approach](https://md.research.elamur.ai/free/en/papers/joshi-2018-sarcasm-target-identification-dataset-introductory/) - [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/) - [Your Sentiment Precedes You: Using an author's historical tweets to predict sarcasm](https://md.research.elamur.ai/free/en/papers/khattri-2015-your-sentiment-precedes-you-author/) - [A Large Self-Annotated Corpus for Sarcasm](https://md.research.elamur.ai/free/en/papers/khodak-2018-large-self-annotated-corpus-sarcasm/) - [When did you become so smart, oh wise one?! 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BibTeX from the library's record of the work. ```bibtex @inproceedings{joshi2015harnessing, title = {{Harnessing Context Incongruity for Sarcasm Detection}}, author = {Joshi, Aditya and Sharma, Vinita and Bhattacharyya, Pushpak}, booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)}, year = {2015}, doi = {10.3115/v1/p15-2124}, url = {https://doi.org/10.3115/v1/p15-2124} } ``` - [← previous Generation and Comprehension of Unambiguous Object Descriptions](https://md.research.elamur.ai/free/en/papers/mao-2015-generation-comprehension-unambiguous-object-descriptions/) - [next → Highway Networks](https://md.research.elamur.ai/free/en/papers/srivastava-2015-highway-networks/) - [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/) ## References 18 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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