[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/prag-2013-riloff-sarcasm-as-contrast-between-positive-2/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/) / Who cites «Sarcasm as Contrast between a Positive Sentiment and Negative Situation» · page 2

# Who cites «Sarcasm as Contrast between a Positive Sentiment and Negative Situation» · page 2

165 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/prag-2013-riloff-sarcasm-as-contrast-between-positive/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/prag-2013-riloff-sarcasm-as-contrast-between-positive/) · **2**

1. [Reasoning with Sarcasm by Reading In-Between](https://aclanthology.org/P18-1093/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Sarcasm Analysis Using Conversation Context](https://aclanthology.org/J18-4009/) *— 2018*
3. [Sarcasm Target Identification: Dataset and An Introductory Approach](https://aclanthology.org/L18-1424/) *— 2018 · Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)*
4. [SemEval-2018 Task 3: Irony Detection in English Tweets](https://aclanthology.org/S18-1005/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation*
5. [ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English Tweets](https://aclanthology.org/S18-1105/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation*
6. [WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony](https://aclanthology.org/S18-1090/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation*
7. [We Usually Don''t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter](https://md.mova.elamur.ai/free/en/docs/papers/hee-2018-we-usually-don-t-like/) *— our card · 2018*
8. [\#NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets](https://aclanthology.org/S18-1106/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation*
9. [A Survey on Hate Speech Detection using Natural Language Processing](https://aclanthology.org/W17-1101/) *— 2017 · Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media*
10. [Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog](https://md.mova.elamur.ai/free/en/docs/papers/oraby-2017-are-you-serious-rhetorical-questions/) *— our card · 2017 · Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue*
11. [CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification](https://aclanthology.org/S17-2103/) *— 2017 · Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)*
12. [Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications](https://aclanthology.org/J17-1006/) *— 2017 · Computational Linguistics*
13. [Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network](https://aclanthology.org/P17-1035/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal](https://aclanthology.org/D17-1050/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
15. [Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation](https://aclanthology.org/P17-1155/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://aclanthology.org/C16-1151/) *— 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers*
17. [A Long Short-Term Memory Framework for Predicting Humor in Dialogues](https://aclanthology.org/N16-1016/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
18. [Are Word Embedding-based Features Useful for Sarcasm Detection?](https://aclanthology.org/D16-1104/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
19. [Automatic Identification of Sarcasm Target: An Introductory Approach](https://arxiv.org/abs/1610.07091) *— 2016 · arXiv:1610.07091*
20. [Automatic Recognition of Conversational Strategies in the Service of a Socially-Aware Dialog System](https://aclanthology.org/W16-3647/) *— 2016 · Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
21. [Automatic Sarcasm Detection: A Survey](https://arxiv.org/abs/1602.03426) *— 2016 · arXiv:1602.03426*
22. [Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue](https://md.mova.elamur.ai/free/en/docs/papers/oraby-2016-creating-characterizing-diverse-corpus-sarcasm/) *— our card · 2016 · Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
23. [Deep Learning of Audio and Language Features for Humor Prediction](https://aclanthology.org/L16-1079/) *— 2016 · Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)*
24. [Detecting Sarcasm in Multimodal Social Platforms](https://arxiv.org/abs/1608.02289) *— 2016 · arXiv:1608.02289*
25. [Exploring the Realization of Irony in Twitter Data](https://aclanthology.org/L16-1283/) *— 2016 · Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)*
26. [Fracking Sarcasm using Neural Network](https://aclanthology.org/W16-0425/) *— 2016 · Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis*
27. [Harnessing Cognitive Features for Sarcasm Detection](https://aclanthology.org/P16-1104/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature](https://md.mova.elamur.ai/free/en/docs/papers/joshi-2016-how-challenging-is-sarcasm-versus/) *— our card · 2016 · Proceedings of the Australasian Language Technology Association Workshop 2016*
29. [Leveraging Cognitive Features for Sentiment Analysis](https://aclanthology.org/K16-1016/) *— 2016 · Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning*
30. [Monday mornings are my fave :) \#not Exploring the Automatic Recognition of Irony in English tweets](https://md.mova.elamur.ai/free/en/docs/papers/hee-2016-monday-mornings-are-my-fave/) *— our card · 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers*
31. [Sarcasm Detection : Building a Contextual Hierarchy](https://aclanthology.org/W16-4313/) *— 2016 · Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES)*
32. [Sarcastic Soulmates: Intimacy and irony markers in social media messaging](https://aclanthology.org/2016.lilt-14.7/) *— 2016*
33. [Tweet Sarcasm Detection Using Deep Neural Network](https://aclanthology.org/C16-1231/) *— 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers*
34. [Understanding Satirical Articles Using Common-Sense](https://aclanthology.org/Q16-1038/) *— 2016 · Transactions of the Association for Computational Linguistics*
35. [`Who would have thought of that!'': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection](https://aclanthology.org/W16-5001/) *— 2016 · Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)*
36. [Harnessing Context Incongruity for Sarcasm Detection](https://aclanthology.org/P15-2124/) *— 2015 · 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)*
37. [LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets](https://aclanthology.org/S15-2113/) *— 2015 · Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)*
38. [Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words](https://aclanthology.org/D15-1116/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
39. [SentiRuEval: тестирование систем анализа тональности текстов на русском языке по отношению к заданному объекту](https://dialogue-conf.org/ru/digest/digest2015/) *— 2015 · Компьютерная лингвистика и интеллектуальные технологии: по материалам конференции «Диалог» (2015)*
40. [Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment](https://aclanthology.org/P15-1100/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
41. [Towards a Contextual Pragmatic Model to Detect Irony in Tweets](https://aclanthology.org/P15-2106/) *— 2015 · 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)*
42. [ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm](https://aclanthology.org/S15-2117/) *— 2015 · Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)*
43. [Your Sentiment Precedes You: Using an author''s historical tweets to predict sarcasm](https://aclanthology.org/W15-2905/) *— 2015 · Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis*
44. [+/-EffectWordNet: Sense-level Lexicon Acquisition for Opinion Inference](https://aclanthology.org/D14-1125/) *— 2014 · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
45. [An Account of Opinion Implicatures](https://arxiv.org/abs/1404.6491) *— 2014 · arXiv:1404.6491*
46. [Getting Reliable Annotations for Sarcasm in Online Dialogues](https://aclanthology.org/L14-1046/) *— 2014 · Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)*
47. [Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)](https://aclanthology.org/P14-2084/) *— 2014 · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
48. [Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases](https://aclanthology.org/D14-1127/) *— 2014 · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
49. [Lexical Acquisition for Opinion Inference: A Sense-Level Lexicon of Benefactive and Malefactive Events](https://aclanthology.org/W14-2618/) *— 2014 · Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis*
50. [Measuring Sentiment Annotation Complexity of Text](https://aclanthology.org/P14-2007/) *— 2014 · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
51. [Modelling Sarcasm in Twitter, a Novel Approach](https://aclanthology.org/W14-2609/) *— 2014 · Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis*
52. [Sentiment Propagation via Implicature Constraints](https://aclanthology.org/E14-1040/) *— 2014 · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics*
53. [The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter](https://aclanthology.org/P14-1017/) *— 2014 · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. karanasou-2015-dsunipi-svm-based-approach-sentiment
55. karoui-2015-detection-automatique-de-l-ironie
56. kurihara-2015-trouble-information-extraction-based-bootstrap
57. mishra-2023-pytail-open-source-tool-interactive
58. misra-2016-nlds-ucsc-semeval-2016-task
59. naganna-2025-my-life-is-miserable-have
60. nakaguro-2026-exploring-emotional-nuances-spoken-dialogue
61. saravani-2021-investigation-into-contribution-locally-aggregated
62. schulder-2018-automatically-creating-lexicon-verbal-polarity
63. soni-2022-predicting-long-term-citations-short
64. wehrli-2024-guiding-sentiment-analysis-hierarchical-text
65. zheng-2025-fanchuan-multilingual-graph-structured-benchmark

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/prag-2013-riloff-sarcasm-as-contrast-between-positive/) · **2**

- [← previous Who cites «Sarcasm as Contrast between a Positive Sentiment and Negative Situation»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/prag-2013-riloff-sarcasm-as-contrast-between-positive/)
- [next → Who cites «Annotation Guidelines for Questions under Discussion and Information Structure»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/pragmatics-2018-riester-annotation-guidelines-questions-under/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)