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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

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  1. Binarizer at SemEval-2018 Task 3: Parsing dependency and deep learning for irony detection — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
  2. CASCADE: Contextual Sarcasm Detection in Online Discussion Forums — 2018 · Proceedings of the 27th International Conference on Computational Linguistics
  3. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science — 2018
  4. Deep contextualized word representations for detecting sarcasm and irony — 2018 · Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
  5. Detecting Sarcasm is Extremely Easy ;-) — 2018 · Proceedings of the Workshop on Computational Semantics beyond Events and Roles
  6. INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
  7. IronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social media — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
  8. Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
  9. Reasoning with Sarcasm by Reading In-Between — 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  10. Sarcasm Analysis Using Conversation Context — 2018
  11. Sarcasm Target Identification: Dataset and An Introductory Approach — 2018 · Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
  12. SemEval-2018 Task 3: Irony Detection in English Tweets — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
  13. ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English Tweets — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
  14. WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
  15. We Usually Don''t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter — our card · 2018
  16. \#NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
  17. A Survey on Hate Speech Detection using Natural Language Processing — 2017 · Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media
  18. Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog — our card · 2017 · Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
  19. CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification — 2017 · Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
  20. Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications — 2017 · Computational Linguistics
  21. Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network — 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  22. Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal — 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
  23. Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation — 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  24. A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks — 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
  25. A Long Short-Term Memory Framework for Predicting Humor in Dialogues — 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  26. Are Word Embedding-based Features Useful for Sarcasm Detection? — 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
  27. Automatic Identification of Sarcasm Target: An Introductory Approach — 2016 · arXiv:1610.07091
  28. Automatic Recognition of Conversational Strategies in the Service of a Socially-Aware Dialog System — 2016 · Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue
  29. Automatic Sarcasm Detection: A Survey — 2016 · arXiv:1602.03426
  30. Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue — our card · 2016 · Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue
  31. Deep Learning of Audio and Language Features for Humor Prediction — 2016 · Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
  32. Detecting Sarcasm in Multimodal Social Platforms — 2016 · arXiv:1608.02289
  33. Exploring the Realization of Irony in Twitter Data — 2016 · Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
  34. Fracking Sarcasm using Neural Network — 2016 · Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
  35. Harnessing Cognitive Features for Sarcasm Detection — 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  36. How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature — our card · 2016 · Proceedings of the Australasian Language Technology Association Workshop 2016
  37. Leveraging Cognitive Features for Sentiment Analysis — 2016 · Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning
  38. Monday mornings are my fave :) \#not Exploring the Automatic Recognition of Irony in English tweets — our card · 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
  39. NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets — 2016 · Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
  40. Sarcasm Detection : Building a Contextual Hierarchy — 2016 · Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES)
  41. Sarcastic Soulmates: Intimacy and irony markers in social media messaging — 2016
  42. Tweet Sarcasm Detection Using Deep Neural Network — 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
  43. Understanding Satirical Articles Using Common-Sense — 2016 · Transactions of the Association for Computational Linguistics
  44. `Who would have thought of that!'': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection — 2016 · Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)
  45. DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter — 2015 · Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
  46. Détection automatique de l'ironie dans les tweets en français — 2015 · Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts
  47. Harnessing Context Incongruity for Sarcasm Detection — 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)
  48. LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets — 2015 · Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
  49. Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words — 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
  50. SentiRuEval: тестирование систем анализа тональности текстов на русском языке по отношению к заданному объекту — 2015 · Компьютерная лингвистика и интеллектуальные технологии: по материалам конференции «Диалог» (2015)
  51. Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment — 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)
  52. Towards a Contextual Pragmatic Model to Detect Irony in Tweets — 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)
  53. Trouble information extraction based on a bootstrap approach from Twitter — 2015 · Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation
  54. ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm — 2015 · Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
  55. Your Sentiment Precedes You: Using an author''s historical tweets to predict sarcasm — 2015 · Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
  56. +/-EffectWordNet: Sense-level Lexicon Acquisition for Opinion Inference — 2014 · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  57. An Account of Opinion Implicatures — 2014 · arXiv:1404.6491
  58. Getting Reliable Annotations for Sarcasm in Online Dialogues — 2014 · Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
  59. Humans Require Context to Infer Ironic Intent (so Computers Probably do, too) — 2014 · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
  60. Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases — 2014 · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  61. Lexical Acquisition for Opinion Inference: A Sense-Level Lexicon of Benefactive and Malefactive Events — 2014 · Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
  62. Measuring Sentiment Annotation Complexity of Text — 2014 · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
  63. Modelling Sarcasm in Twitter, a Novel Approach — 2014 · Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
  64. Sentiment Propagation via Implicature Constraints — 2014 · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
  65. The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter — 2014 · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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