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Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language Using Word-Emoji Embeddings

Published:08 May 2023Publication History
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Abstract

Automated sarcasm detection is deemed as a complex natural language processing task and extending it to a morphologically-rich and free-order dominant indigenous Indian language Hindi is another challenge in itself. The scarcity of resources and tools such as annotated corpora, lexicons, dependency parser, Part-of-Speech tagger, and benchmark datasets engorge the linguistic challenges of sarcasm detection in low-resource languages like Hindi. Furthermore, as context incongruity is imperative to detect sarcasm, various linguistic, aural and visual cues can be used to predict target utterance as sarcastic. While pre-trained word embeddings capture the meanings, semantic relationships and different types of contexts in the form of word representations, emojis can also render useful contextual information, analogous to human facial expressions, for gauging sarcasm. Thus, the goal of this research is to demonstrate the use of a hybrid deep learning model trained using two embeddings, namely word and emoji embeddings to detect sarcasm. The model is validated on a Hindi tweets dataset, Sarc-H, manually annotated with sarcastic and non-sarcastic labels. The preliminary results clearly depict the importance of using emojis for sarcasm detection, with our model attaining an accuracy of 97.35% with an F-score of 0.9708. The research validates that automated feature engineering facilitates efficient and repeatable predictive model for detecting sarcasm in indigenous, low-resource languages.

REFERENCES

  1. [1] Cambria E., Schuller B., Xia Y., and Havasi C.. 2013. New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems 28, 2 (2013), 1521.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Kumar A., Srinivasan K., Cheng W. H., and Zomaya A. Y.. 2020. Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Information Processing & Management 57, 1 (2020), 102141.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Kumar A.. 2021. Contextual semantics using hierarchical attention network for sentiment classification in social internet-of-things. Multimed. Tools Appl. https://doi.org/10.1007/s11042-021-11262-8Google ScholarGoogle Scholar
  4. [4] Jain D., Kumar A., and Garg G.. 2020. Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN. Applied Soft Computing 91 (2020), 106198.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Akhtar M. S., Ekbal A., and Cambria E.. 2020. How intense are you? Predicting intensities of emotions and sentiments using stacked ensemble [application notes]. IEEE Computational Intelligence Magazine 15, 1 (2020), 6475.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Poria S., Chaturvedi I., Cambria E., and Hussain A.. 2016. Convolutional MKL based multimodal emotion recognition and sentiment analysis. In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 439448.Google ScholarGoogle Scholar
  7. [7] Majumder N., Poria S., Peng H., Chhaya N., Cambria E., and Gelbukh A.. 2019. Sentiment and sarcasm classification with multitask learning. IEEE Intelligent Systems 34, 3 (2019), 3843.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Ghosh D., Guo W., and Muresan S.. 2015. Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 10031012.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Kumar A., Dikshit S., and Albuquerque V. H. C.. 2021. Explainable artificial intelligence for sarcasm detection in dialogues. Wireless Communications and Mobile Computing (2021).Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Kumar A. and Garg G.. 2020. The multifaceted concept of context in sentiment analysis. In Cognitive Informatics and Soft Computing. Springer, Singapore, 413421.Google ScholarGoogle Scholar
  11. [11] Bliss-Carroll N. L.. 2016. The nature, function, and value of emojis as contemporary tools of digital interpersonal communication.Google ScholarGoogle Scholar
  12. [12] Bharti S. K., Babu K. S., and Jena S. K.. 2015. Parsing-based sarcasm sentiment recognition in Twitter data. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 13731380.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Kumar A. and Garg G.. 2019. Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets. Journal of Ambient Intelligence and Humanized Computing (2019), 116.Google ScholarGoogle Scholar
  14. [14] Davidov D., Tsur O., and Rappoport A.. 2010. Semi-supervised recognition of sarcasm in Twitter and Amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning. 107116.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Parshad R. D., Bhowmick S., Chand V., Kumari N., and Sinha N.. 2016. What is India speaking? Exploring the “Hinglish” invasion. Physica A: Statistical Mechanics and its Applications 449 (2016), 375389.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Kumar A. and Albuquerque V. H. C.. 2021. Sentiment analysis using XLM-R transformer and zero-shot transfer learning on resource-poor Indian language. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 20, 5, Article 90 (September 2021), 13 pages. DOI: https://doi.org/10.1145/3461764Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Bharti S. K., Babu K. S., and Jena S. K.. 2017. Harnessing online news for sarcasm detection in Hindi tweets. In International Conference on Pattern Recognition and Machine Intelligence. Springer, Cham. 679686.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Desai N. and Dave A. D.. 2016. Sarcasm detection in Hindi sentences using support vector machine. International Journal 4, 7 (2016), 815.Google ScholarGoogle Scholar
  19. [19] Kumar A., Bhatia M. P. S., and Sangwan S. R.. 2021. Rumour detection using deep learning and filter-wrapper feature selection in benchmark Twitter dataset. Multimedia Tools and Applications. 118.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Sangwan S. R. and Bhatia M. P. S.. 2020. D-BullyRumbler: A safety rumble strip to resolve online denigration bullying using a hybrid filter-wrapper approach. Multimedia Systems. 117.Google ScholarGoogle Scholar
  21. [21] Kumar A. and Jaiswal A.. 2020. Deep learning based sentiment classification on user-generated big data. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) 13, 5 (2020), 10471056.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Kumar A. and Sachdeva N.. 2020. Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data. Multimedia Systems. 115.Google ScholarGoogle Scholar
  23. [23] Eisner B., Rocktäschel T., Augenstein I., Bošnjak M., and Riedel S.. 2016. emoji2vec: Learning emoji representations from their description. arXiv preprint arXiv:1609.08359.Google ScholarGoogle Scholar
  24. [24] Gu J., Wang Z., Kuen J., Ma L., Shahroudy A., Shuai B., Liu T., Wang X., Wang G., Cai J., and Chen T.. 2018. Recent advances in convolutional neural networks. Pattern Recognition 1 (2018 May), 77:354–77.Google ScholarGoogle Scholar
  25. [25] Hochreiter S. and Schmidhuber J.. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 17351780.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. [26] Tepperman J., Traum D., and Narayanan S.. 2006. “Yeah Right”: Sarcasm recognition for spoken dialogue systems. In Ninth International Conference on Spoken Language Processing.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Kreuz R. and Caucci G.. 2007. Lexical influences on the perception of sarcasm. In Proceedings of the Workshop on Computational Approaches to Figurative Language. 14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] González-Ibánez R., Muresan S., and Wacholder N.. 2011. Identifying sarcasm in Twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 581586.Google ScholarGoogle Scholar
  29. [29] Riloff E., Qadir A., Surve P., De Silva L., Gilbert N., and Huang R.. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 704714.Google ScholarGoogle Scholar
  30. [30] Liebrecht C. C., Kunneman F. A., and van Den Bosch A. P. J.. 2013. The perfect solution for detecting sarcasm in tweets# not.Google ScholarGoogle Scholar
  31. [31] Joshi A., Sharma V., and Bhattacharyya P.. 2015. Harnessing context incongruity for sarcasm detection. In 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). 757762.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Joshi A., Tripathi V., Bhattacharyya P., and Carman M.. 2016. Harnessing sequence labeling for sarcasm detection in dialogue from TV series ‘Friends’. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. 146155.Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Kumar A. and Garg G.. 2020. Sarcasm detection using feature-variant learning models. In Proceedings of ICETIT 2019. Springer, Cham. 683693.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Schifanella R., de Juan P., Tetreault J., and Cao L.. 2016. Detecting sarcasm in multimodal social platforms. In Proceedings of the 24th ACM International Conference on Multimedia. 11361145.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Castro S., Hazarika D., Pérez-Rosas V., Zimmermann R., Mihalcea R., and Poria S.. 2019. Towards multimodal sarcasm detection (an _Obviously_ perfect paper). arXiv preprint arXiv:1906.01815.Google ScholarGoogle Scholar
  36. [36] Kumar A. and Garg G.. 2019. Sarc-m: Sarcasm detection in typo-graphic memes. In International Conference on Advances in Engineering Science Management & Technology (ICAESMT)-2019, Uttaranchal University, Dehradun, India.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Joshi A., Tripathi V., Patel K., Bhattacharyya P., and Carman M.. 2016. Are word embedding-based features useful for sarcasm detection?. arXiv preprint arXiv:1610.00883.Google ScholarGoogle Scholar
  38. [38] Ghosh A. and Veale T.. 2016. Fracking sarcasm using neural network. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 161169.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Joshi A., Bhattacharyya P., and Carman M. J.. 2017. Automatic sarcasm detection: A survey. ACM Computing Surveys (CSUR) 50, 5 (2017), 122.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Kumar A., Sangwan S. R., Arora A., Nayyar A., and Abdel-Basset M.. 2019. Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE Access 7 (2019), 2331923328.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Alayba A. M., Palade V., England M., and Iqbal R.. 2018. A combined CNN and LSTM model for Arabic sentiment analysis. In International Cross-domain Conference for Machine Learning and Knowledge Extraction. Springer, Cham. 179191.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. [42] Ptáček T., Habernal I., and Hong J.. 2014. Sarcasm detection on Czech and English Twitter. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 213223.Google ScholarGoogle Scholar
  43. [43] Liu P., Chen W., Ou G., Wang T., Yang D., and Lei K.. 2014. Sarcasm detection in social media based on imbalanced classification. In International Conference on Web-Age Information Management. Springer, Cham. 459471.Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Justo R., Alcaide J. M., Torres M. I., and Walker M.. 2018. Detection of sarcasm and nastiness: New resources for Spanish language. Cognitive Computation 10, 6 (2018), 11351151.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Lunando E. and Purwarianti A.. 2013. Indonesian social media sentiment analysis with sarcasm detection. In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 195198.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Swami S., Khandelwal A., Singh V., Akhtar S. S., and Shrivastava M.. 2018. A corpus of English-Hindi code-mixed tweets for sarcasm detection. arXiv preprint arXiv:1805.11869.Google ScholarGoogle Scholar
  47. [47] Mikolov T., Sutskever I., Chen K., Corrado G., and Dean J.. 2013. Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546.Google ScholarGoogle Scholar
  48. [48] Felbo B., Mislove A., Søgaard A., Rahwan I., and Lehmann S.. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. arXiv preprint arXiv:1708.00524.Google ScholarGoogle Scholar
  49. [49] Bergstra J., Yamins D., and Cox D.. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International Conference on Machine Learning. PMLR, 115123.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. [50] Raunak V., Gupta V., and Metze F.. 2019. Effective dimensionality reduction for word embeddings. In Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019). 235243.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Bharti S. K. and Babu K. S.. 2018. Sarcasm as a contradiction between a tweet and its temporal facts: A pattern-based approach. International Journal on Natural Language Computing (IJNLC) 7 (2018).Google ScholarGoogle Scholar
  52. [52] Bharti S. K., Babu K. S., and Raman R.. 2017. Context-based sarcasm detection in Hindi tweets. In Ninth International Conference on Advances in Pattern Recognition (ICAPR). 2017.Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Katyayan P. and Joshi N.. 2021. Performance evaluation of machine learning algorithms for detecting Hindi sarcasm. In 2021 Bharatiya Vaigyanik Evam Audyogik Anusandhan Patrika 29, 1 (2021), 4348.Google ScholarGoogle Scholar

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          • Published in

            cover image ACM Transactions on Asian and Low-Resource Language Information Processing
            ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 5
            May 2023
            653 pages
            ISSN:2375-4699
            EISSN:2375-4702
            DOI:10.1145/3596451
            Issue’s Table of Contents

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            Publication History

            • Published: 8 May 2023
            • Online AM: 10 August 2022
            • Accepted: 15 February 2022
            • Received: 16 October 2021
            Published in tallip Volume 22, Issue 5

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