Review of automatic sarcasm detection

Sentiment Analysis has become a significant research matter for its probable in tapping into the vast amount of opinions generated by the people. Sentiment analysis deals with the computational conduct of opinion, sentiment within the text. People sometimes uses sarcastic text to express their opinion within the text. Sarcasm is a type of communication act in which the people write the contradictory of what they mean in reality. The intrinsically vague nature of sarcasm sometimes makes it hard to understand. Recognizing sarcasm can promote many sentiment analysis applications. Automatic detecting sarcasm is an approach for predicting sarcasm in text. In this paper we have tried to talk of the past work that has been done for detecting sarcasm in the text. This paper talk of approaches, features, datasets, and issues associated with sarcasm detection. Performance values associated with the past work also has been discussed. Various tables that present different dimension of past work like dataset used, features, approaches, performance values has also been discussed.

[1]  J. Lucariello Situational irony: A concept of events gone awry. , 1994 .

[2]  Paolo Rosso,et al.  On the difficulty of automatically detecting irony: beyond a simple case of negation , 2014, Knowledge and Information Systems.

[3]  A. Katz,et al.  Are There Necessary Conditions for Inducing a Sense of Sarcastic Irony? , 2012 .

[4]  Elena Filatova,et al.  Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing , 2012, LREC.

[5]  Pushpak Bhattacharyya,et al.  How Do Cultural Differences Impact the Quality of Sarcasm Annotation?: A Case Study of Indian Annotators and American Text , 2016, LaTeCH@ACL.

[6]  Ari Rappoport,et al.  Semi-Supervised Recognition of Sarcasm in Twitter and Amazon , 2010, CoNLL.

[7]  Paolo Rosso,et al.  Making objective decisions from subjective data: Detecting irony in customer reviews , 2012, Decis. Support Syst..

[8]  Nina Wacholder,et al.  Identification of nonliteral language in social media: A case study on sarcasm , 2016, J. Assoc. Inf. Sci. Technol..

[9]  Reza Zafarani,et al.  Sarcasm Detection on Twitter: A Behavioral Modeling Approach , 2015, WSDM.

[10]  Byron C. Wallace,et al.  Humans Require Context to Infer Ironic Intent (so Computers Probably do, too) , 2014, ACL.

[11]  Masnizah Mohd,et al.  Recognition of Sarcasms in Tweets Based on Concept Level Sentiment Analysis and Supervised Learning Approaches , 2014, PACLIC.

[12]  Iván Vladimir Meza Ruíz,et al.  Character and Word Baselines for Irony Detection in Spanish Short Texts , 2016 .

[13]  Renata Vieira,et al.  Some clues on irony detection in tweets , 2013, WWW '13 Companion.

[14]  Tony Veale,et al.  Detecting Ironic Intent in Creative Comparisons , 2010, ECAI.

[15]  Marilyn A. Walker,et al.  Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue , 2013, ArXiv.

[16]  Tomoaki Ohtsuki,et al.  Sarcasm Detection in Twitter: "All Your Products Are Incredibly Amazing!!!" - Are They Really? , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[17]  Sanjay Kumar Jena,et al.  Parsing-based sarcasm sentiment recognition in Twitter data , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[18]  Dimitris Spathis,et al.  A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets , 2016, Eng. Appl. Artif. Intell..

[19]  David Bamman,et al.  Contextualized Sarcasm Detection on Twitter , 2015, ICWSM.

[20]  Dirk Hovy,et al.  Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations , 2016, ACL.

[21]  Debanjan Ghosh,et al.  Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words , 2015, EMNLP.

[22]  Pushpak Bhattacharyya,et al.  Harnessing Cognitive Features for Sarcasm Detection , 2016, ACL.

[23]  Nina Wacholder,et al.  Identifying Sarcasm in Twitter: A Closer Look , 2011, ACL.

[24]  Davide Buscaldi,et al.  From humor recognition to irony detection: The figurative language of social media , 2012, Data Knowl. Eng..

[25]  Ari Rappoport,et al.  ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews , 2010, ICWSM.

[26]  Pushpak Bhattacharyya,et al.  Are Word Embedding-based Features Useful for Sarcasm Detection? , 2016, EMNLP.

[27]  Teresa Gonçalves,et al.  How Does Irony Affect Sentiment Analysis Tools? , 2015, EPIA.

[28]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[29]  Jun Hong,et al.  Sarcasm Detection on Czech and English Twitter , 2014, COLING.

[30]  Diana Maynard,et al.  Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. , 2014, LREC.

[31]  Byron C. Wallace,et al.  Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment , 2015, ACL.

[32]  Pushpak Bhattacharyya,et al.  Harnessing Context Incongruity for Sarcasm Detection , 2015, ACL.

[33]  Pushpak Bhattacharyya,et al.  Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series ‘Friends’ , 2016, CoNLL.

[34]  Elisabetta Fersini,et al.  Detecting irony and sarcasm in microblogs: The role of expressive signals and ensemble classifiers , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[35]  Ellen Riloff,et al.  Sarcasm as Contrast between a Positive Sentiment and Negative Situation , 2013, EMNLP.

[36]  Tomoaki Ohtsuki,et al.  Opinion mining in Twitter: How to make use of sarcasm to enhance sentiment analysis , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[37]  David R. Traum,et al.  "yeah Right": Sarcasm Recognition for Spoken Dialogue Systems , 2006, INTERSPEECH.

[38]  R. Kreuz,et al.  Lexical Influences on the Perception of Sarcasm , 2007 .

[39]  Véronique Hoste,et al.  Exploring the Realization of Irony in Twitter Data , 2016, LREC.

[40]  Byron C. Wallace Computational irony: A survey and new perspectives , 2013, Artificial Intelligence Review.

[41]  Antal van den Bosch,et al.  The perfect solution for detecting sarcasm in tweets #not , 2013, WASSA@NAACL-HLT.

[42]  Pushpak Bhattacharyya,et al.  Your Sentiment Precedes You: Using an author’s historical tweets to predict sarcasm , 2015, WASSA@EMNLP.

[43]  Paolo Rosso,et al.  SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter , 2015, *SEMEVAL.

[44]  Paolo Rosso,et al.  A multidimensional approach for detecting irony in Twitter , 2013, Lang. Resour. Evaluation.

[45]  Ayu Purwarianti,et al.  Indonesian social media sentiment analysis with sarcasm detection , 2013, 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[46]  M. Inés Torres,et al.  Extracting relevant knowledge for the detection of sarcasm and nastiness in the social web , 2014, Knowl. Based Syst..

[47]  Zhijian Wu,et al.  Twitter Sarcasm Detection Exploiting a Context-Based Model , 2015, WISE.

[48]  Rossano Schifanella,et al.  Detecting Sarcasm in Multimodal Social Platforms , 2016, ACM Multimedia.

[49]  Andrew Rosenberg,et al.  "sure, I Did the Right Thing": a System for Sarcasm Detection in Speech , 2013, INTERSPEECH.

[50]  Paolo Rosso,et al.  Applying Basic Features from Sentiment Analysis for Automatic Irony Detection , 2015, IbPRIA.

[51]  R. Giora On irony and negation , 1995 .

[52]  Tony Veale,et al.  Fracking Sarcasm using Neural Network , 2016, WASSA@NAACL-HLT.

[53]  Byron C. Wallace,et al.  Modelling Context with User Embeddings for Sarcasm Detection in Social Media , 2016, CoNLL.

[54]  Deirdre Wilson,et al.  The pragmatics of verbal irony: Echo or pretence? , 2006 .

[55]  Pushpak Bhattacharyya,et al.  Automatic Identification of Sarcasm Target: An Introductory Approach , 2016, ArXiv.