Advertisement

A Survey on Machine Learning and Deep Learning Based Approaches for Sarcasm Identification in Social Media

  • Bhumi ShahEmail author
  • Margil Shah
Conference paper
  • 30 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 52)

Abstract

Today, sentiment analysis is a basic way through which one can get an idea regarding opinion, attitude and emotion towards person or aspects or products or services, etc. In the last several years, researchers are working on a technique to analyse social media data and social chain to identify the undisclosed information from it and to make meaningful patterns and decisions through it. In sentiment analysis, sarcasm is one kind of emotion which contains the contents that are opposite of what you really want to say. People use it to show disrespect or to taunt someone. Sarcasm is useful to show silliness and to be entertaining. Sarcasm can be expressed verbally or through certain gestural clues like rolling of the eyes or raising the eyebrows, etc. A number of ways are implemented to detect sarcasm. In this paper, we try to explain current and trending ways which are used to detect sarcasm.

Keywords

Sentiment Analysis opinion mining Sarcasm detection Machine learning Deep learning 

References

  1. 1.
    Tang D, Liu T, Qin B (2015) Document modeling with gated recurrent neural network for sentiment classification. In: Conference on empirical methods in natural language processing, pp 1422–1432Google Scholar
  2. 2.
    Bharti S, Jena S, Babu K (2015) Parsing based Sarcasm sentiment recognition in tweeter data. In: International conference on advances in social networks analysis and mining, Paris, France, 25–28 Aug 2015Google Scholar
  3. 3.
    Sindhu C, Vadivu G, Mandala V (2018) A comprehensive study on sarcasm detection techniques in sentiment analysis. Int J Pure Appl Math 118:433–442Google Scholar
  4. 4.
    Barbieri F, Saggion H, Ronzano F (2014) Italian irony detection in twitter: a first approach. In: The first Italian conference on computational linguistics, pp 28–32Google Scholar
  5. 5.
    Joshi A, Tripathi V, Bhattacharyya P, Mark C (2016) Are word embedding-based features for sarcasm detection? In: Conference on empirical methods in natural language processing, pp 1006–1011Google Scholar
  6. 6.
    Wang Z, Zhijin W, Ruimin W, Ren Y (2015) Twitter sarcasm detection exploiting a context-based model. In: Web information systems engineering, Springer, pp 77–91Google Scholar
  7. 7.
    Ghosh D, Guo W, Muresan S (2015) Sarcastic or not: word embeddings to predict the literal or sarcastic meaning of words. In: Conference on empirical methods in natural language processing, pp 1003–1012Google Scholar
  8. 8.
    Majumder N, Peng H, Chhaya N, Poria S, Gelbukh A, Cambria E (2019) Sentiment and sarcasm classification with multitask learning. IEEE Intell Syst 34(3):38–43CrossRefGoogle Scholar
  9. 9.
    Peng CC, Lakis M, Pan JW (2015) Detecting sarcasm in textGoogle Scholar
  10. 10.
    Clews P, Kuzma J (2017) Rudimentary lexicon based method for sarcasm detection. Int J Acad Res Reflection 5(4):24–33Google Scholar
  11. 11.
    Joshi A, Carman M (2017) Automatic sarcasm detection: a survey. ACM Comput Surv 50(5):1–22CrossRefGoogle Scholar
  12. 12.
    Joshi A, Bhattacharyya P, Sharma V (201) Harnessing context incongruity for sarcasm detection. In: 7th International joint conference on natural language processing, vol 2, pp 757–762Google Scholar
  13. 13.
    Hamdi A, Shaban K, Zainal A (2018) Clasenti: a class-specific sentiment analysis framework. ACM Trans Asian Low-Resour Lang Inf Process (TALLIP) 17(4):1–28CrossRefGoogle Scholar
  14. 14.
    Hai Z, Cong G, Chang K, Cheng P, Miao C (2017) Analyzing sentiments in one go: a supervised joint topic modeling approach. IEEE Trans Knowl Data Eng 29(6):1172–1185CrossRefGoogle Scholar
  15. 15.
    Kolchyna O, Souza T, Aste T, Treleaven P (2015) Twitter sentiment analysis: Lexicon method, machine learning method and their combination. arXiv preprintGoogle Scholar
  16. 16.
    Ozgur A (2004) Supervised and unsupervised machine learning techniques for text document categorization. Bogaziçi University, IstanbulGoogle Scholar
  17. 17.
    Hemmatian F, Sohrabi MK (2017) A survey on classification techniques for opinion mining and sentiment analysis. Artif Intell Rev 1–51Google Scholar
  18. 18.
    Davidov D, Rappoport A, Tsur O (2010) Semi-supervised recognition of sarcastic sentences in twitter and amazon. In: 23rd International conference on computational natural language learning, pp 107–116Google Scholar
  19. 19.
    Sridhar R (2017) Emotion and sarcasm identification of Posts from Facebook data using a Hybrid approach. ICTACT J Soft Comput 7(2)Google Scholar
  20. 20.
    Ptacek T, Habernal I, Hong J (2014) Sarcasm detection on czech and english twitter. In: 25th International conference on computational linguistics, pp 213–223Google Scholar
  21. 21.
    Bamman D., Smith N.: Contextualized sarcasm detection on twitter. In: 9th International AAAI conference on web and social mediaGoogle Scholar
  22. 22.
    Bharti S, Jena S, Babu K (2015) Parsing-based sarcasm sentiment recognition in twitter data. In: IEEE/ACM International conference on advances in social networks analysis and mining. ACM, pp 1373–1380Google Scholar
  23. 23.
    Mukherjee S, Bala P (2017) Detecting sarcasm in customer tweets: an NLP based approach. Industr Manage Data Syst 117(6):1109–1126CrossRefGoogle Scholar
  24. 24.
    Saha S, Yadav J, Ranjan P (2017) Proposed approach for sarcasm detection in twitter. Indian J Sci Technol 10:25Google Scholar
  25. 25.
    Purwarianti A, Lunando E (2013) Indonesian social media sentiment analysis with sarcasm detection. In: International conference on advanced computer science and information systems. IEEE, New York, pp 195–198Google Scholar
  26. 26.
    Tungthamthiti P, Mohd M, Kiyoaki S (2014) Recognition of sarcasms in tweets based on concept level sentiment analysis and supervised learning approaches. In: Pacific Asia conference on language, information and computing, pp 404–413Google Scholar
  27. 27.
    Dharwal P (2017) Automatic sarcasm detection using feature selection. In: 3rd International conference on applied and theoretical computing and communication technology (iCATccT), IEEEGoogle Scholar
  28. 28.
    Amir S, Wallace B, Lyu H, Silva P (2016) Modelling context with user embeddings for sarcasm detection in social mediaGoogle Scholar
  29. 29.
    Joshi A, Bhattacharyya P, Carman M, Saraswati J, Shukla R (2016) How do cultural differences impact the quality of sarcasm annotation? A case study of indian annotators and american text. In: 10th SIGHUM Workshop Language Technology Cultural Heritage, Social Sciences, Humanities, pp 95–99Google Scholar
  30. 30.
    Dai Y, Wang G (2018) Analyzing tongue images using a conceptual alignment deep autoencoder. In: IEEE Access, pp 1–1.  https://doi.org/10.1109/access.2017.2788849
  31. 31.
    Olivia N, Laradji IH (2014) Robust Feature Extraction Algorithm for Sarcasm Detection in Debates. In 6th International conference on data miningGoogle Scholar
  32. 32.
    Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdisc Rev Data Min Knowl Discov 8(4):e1253CrossRefGoogle Scholar
  33. 33.
    Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211CrossRefGoogle Scholar
  34. 34.
    Hochreiter S, Schmidhuber J (1997) Long short-term memory. In: Neural computation, pp 1735–1780Google Scholar
  35. 35.
    Kumar A, Sangwan S, Arora A, Nayyar A, Abdel-Basset M (2019) Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. In: IEEE Access, pp 23319–23328Google Scholar
  36. 36.
    Poria, S, Chaturvedi I, Cambria E, Hussain A (2016) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: 6th international conference on data mining (ICDM). IEEE, pp 439–448Google Scholar
  37. 37.
    Poria S, Vij P, Hazarika D, Cambria E (2016) A deeper look into sarcastic tweets using deep convolutional neural networks. In: International conference on computational linguistics, pp 1601–1612Google Scholar
  38. 38.
    Kabir MY, Madria S (2019) A deep learning approach for tweet classification and rescue scheduling for effective disaster management. In: 27th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 269–278Google Scholar
  39. 39.
    Mishra A, Bhattacharyya P, Dey K (2017) Learning cognitive features from gaze data for sentiment and sarcasm classification using convolutional neural network. In: Annual meeting of the association for computational linguistics, pp 377–387Google Scholar
  40. 40.
    Majumder N, Mihalcea R, Poria S, Hazarika D, Gelbukh A, Cambria E (2019) DialogueRNN: an attentive RNN for emotion detection in conversations. In: AAAI conference on artificial intelligence, vol 33, pp 6818–6825Google Scholar
  41. 41.
    Busso C, Bulut M, Lee C, Kazemzadeh A, Mower E, Kim S, Chang JN, Lee S, Narayanan S (2008) Interactive emotional dyadic motion capture database. In: Language resources and evaluation, pp 335–359Google Scholar
  42. 42.
    Schuller B Valster M, Eyben, F, Cowie R, Pantic M (2012) The continuous audio/visual emotion challenge. In: 14th ACM international conference on multimodal interaction, pp 449–456. New York, USAGoogle Scholar
  43. 43.
    Hazarika D, Poria S, Gorantla S, Cambria E, Zimmermann R, Mihalcea R (2018) CASCADE: contextual sarcasm detection in online discussion forums. In: International conference of computational linguistics, pp 1837–1848Google Scholar
  44. 44.
    Zhang M, Fu G, Zhang Y (2016) Tweet sarcasm detection using deep neural network. In: The 26th international conference on computational linguistics, pp 2449–2460Google Scholar
  45. 45.
    Ghosh A, Veale T (2016) Fracking sarcasm using neural network. In: 7th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 161–169Google Scholar
  46. 46.
    Liebrecht C, APJ V, Kunneman F (2013) The perfect solution for detecting sarcasm in tweets# not. In 4th Workshop on computational approaches to subjectivity, sentiment and social media analysis, Atlanta, pp 29–37Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Gandhinagar Institute of TechnologyKalolIndia

Personalised recommendations