Different Approaches in Sarcasm Detection: A Survey

Sarcasm is an unwelcome impact or a linguistic circumstance to express histrionic and bitterly opinions. In sarcasm single word in a sentence can flip the polarity of positive or negative statement totally. Therefore sarcasm occurs when there is an imbalance between text and context. This paper surveys different approaches and datasets for sarcasm detection. Different approaches surveyed are statistical approach, rule based approach, classification approach and deep learning approach. It also gives insight to different methodologies used in past for sarcasm detection. After surveying we found deep learning is generating a good result as compare to other approaches.

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

[2]  Chaitali Chandankhede,et al.  Literature survey of sarcasm detection , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

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

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

[5]  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).

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

[7]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

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

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

[10]  Pushpak Bhattacharyya,et al.  Automatic Sarcasm Detection , 2016, ACM Comput. Surv..

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

[12]  Fabio Crestani,et al.  Like It or Not , 2016, ACM Comput. Surv..

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

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

[15]  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).

[16]  Bhawna Rajput,et al.  Polarity detection of sarcastic political tweets , 2014, 2014 International Conference on Computing for Sustainable Global Development (INDIACom).

[17]  Peng Liu,et al.  Sarcasm Detection in Social Media Based on Imbalanced Classification , 2014, WAIM.

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

[19]  Ari Rappoport,et al.  Efficient Unsupervised Discovery of Word Categories Using Symmetric Patterns and High Frequency Words , 2006, ACL.

[20]  Tomoaki Ohtsuki,et al.  A Pattern-Based Approach for Sarcasm Detection on Twitter , 2016, IEEE Access.

[21]  Elisabeth Camp.  Sarcasm, Pretense, and The Semantics/ Pragmatics Distinction ∗ , 2012 .

[22]  H. Grice.  Logic and conversation , 1975 .

[23]  Ramiz M. Aliguliyev,et al.  QMOS: Query-based multi-documents opinion-oriented summarization , 2018, Inf. Process. Manag..

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

[25]  Erik Cambria,et al.  A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks , 2016, COLING.

[26]  Horacio Saggion,et al.  Modelling Sarcasm in Twitter, a Novel Approach , 2014, WASSA@ACL.