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Identification of sarcasm using word embeddings and hyperparameters tuning

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References

  • Tepperman, J., Traum, D., & Narayanan, S. “ yeah right”: Sarcasm recognition for spoken dialogue systems. In Ninth international conference on spoken language processing (2006).
  • Kreuz, R. J., & Caucci, G. M. Lexical influences on the perception of sarcasm. In Proceedings of the workshop on computational approaches to figurative language (pp. 1-4) (2007).
  • Clark, H. H., & Gerrig, R. J. On the pretense theory of irony. American Psychological Association (1984).
  • Poria, S., Cambria, E., Hazarika, D., & Vij, P. A deeper look into sarcastic tweets using deep convolutional neural networks (2016). arX-ivpreprint rXiv:1610.08815.
  • Mehndiratta, P. Analysis of online social networks for the identification of sarcasm. In Social network analytics for contemporary business organizations (pp. 92-105) (2018). IGI Global. doi: 10.4018/978-1-5225-5097-6.ch006
  • Mehndiratta, P., Sachdeva, S., & Soni, D. Detection of sarcasm in text data using deep convolutional neural networks. Scalable Computing: Practice and Experience, 18 (3), 219-228 (2017). doi: 10.7494/csci.2017.18.3.1413
  • Driscoll, B. Sentiment analysis and the literary festival audience. Continuum, 29(6), 861-873 (2015). doi: 10.1080/10304312.2015.1040729
  • Durugkar, S., & Poonia, R. C. Optimum utilization of natural resources for home garden using wireless sensor networks. Journal of Information and Optimization Sciences, 38(6), 1077-1085 (2017). doi: 10.1080/02522667.2017.1380391
  • Poonia, R. C. Viability Analysis of TwoRayGround and Nakagami Model for Vehicular Ad-Hoc Networks. International Journal of Applied Evolutionary Computation (IJAEC), 8(2), 44-57 (2017). doi: 10.4018/IJAEC.2017040103
  • He, W., Tian, X., Chen, Y., & Chong, D. Actionable social media competitive analytics for understanding customer experiences. Journal of Computer Information Systems, 56(2), 145-155 (2016). doi: 10.1080/08874417.2016.1117377
  • Gonzalez-Ibanez, R., Muresan, S., & Wacholder, N. Identifying sarcasm in twitter: a closer look. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies: Short papers-volume 2 (pp. 581-586) (2011).
  • Davidov, D., Tsur, O., & Rappoport, A. Semi-supervised recognition of sarcastic sentences in twitter and amazon. In Proceedings of the fourteenth conference on computational natural language learning (pp. 107–116) (2010).
  • Tsur, O., Davidov, D., & Rappoport, A. a great catchy name: Semisupervised recognition of sarcastic sentences in online product reviews. In Fourth international aaai conference on weblogs and social media (2010).
  • Liebrecht, C., Kunneman, F., & van Den Bosch, A. The perfect solution for detecting sarcasm in tweets# not. Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WAS-SA) (2013).
  • Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., & Huang, R. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 704-714) (2013).
  • Reyes, A., Rosso, P., & Veale, T. A multidimensional approach for detecting irony in twitter. Language resources and evaluation, 47 (1), 239-268 (2013). doi: 10.1007/s10579-012-9196-x
  • Filatova, E. Irony and sarcasm: Corpus generation and analysis using crowdsourcing. In LREC (pp. 392-398) (2012). European Language Resources Association (ELRA).
  • Dhaka, V. S., Poonia, R. C., & Raja, L. The Realistic Mobility Evaluation of Vehicular Ad-Hoc Network for Indian Automotive Networks. International Journal of Ad hoc, Sensor & Ubiquitous Computing, 5(2), 1 (2014).
  • Felbo, B., Mislove, A., Sgaard, A., Rahwan, I., & Lehmann, S. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm (2017). arXiv preprint arXiv:1708.00524
  • Ghosh, D., & Muresan, S. “with 1 follower I must be AWESOME : P.” exploring the role of irony markers in irony recognition. In Proceedings of the twelfth international conference on web and social media, ICWSM 2018, stanford, california, usa, june 25-28 (2018).
  • Ghosh, D., Fabbri, A. R., & Muresan, S. The role of conversation context for sarcasm detection in online interactions. In SIGDIAL conference (pp. 186-196) (2017). Association for Computational Linguistics.
  • Tay, Y., Tuan, L. A., Hui, S. C., & Su, J. Reasoning with sarcasm by reading in between (2018). arXiv preprint arXiv:1805.02856.
  • Gupta, S., Poonia, R. C., Singh, V., & Raja, L. Tier Application in Multi-Cloud Databases to Improve Security and Service Availability. In Handbook of Research on Cloud Computing and Big Data Applications in IoT (pp. 82-93) (2019). IGI Global.
  • Wallace, B. C., Choe, D. K., Kertz, L., & Charniak, E. Humans require context to infer ironic intent (so computers probably do, too). In ACL (2) (pp. 512-516) (2014). The Association for Computer Linguistics.
  • Singh, V., Poonia, R. C., Raja, L., Sharma, G., & Trivedi, N. K. Source Redundancy Management and Host Intrusion Detection in Wireless Sensor Networks. Recent Patents on Computer Science, 12(1) (2019).
  • Khodak, M., Saunshi, N., & Vodrahalli, K. A large self-annotated corpus for sarcasm (2017). arXiv preprint arXiv:1704.05579
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. Efficient estimation of word representations in vector space (2013). In ICLR (workshop).
  • Pennington, J., Socher, R., & Manning, C. D. Glove: Global vectors for word representation. In EMNLP (pp. 1532-1543) (2014). ACL.
  • Dhaka, V. S., Poonia, R. C., & Raja, L. A literature review on dedicated short range communication for intelligent transport. International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), 3(9), 1066-1071 (2013).
  • LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 (11), 2278-2324 (1998). doi: 10.1109/5.726791
  • Hochreiter, S., & Schmidhuber, J. Long short-term memory. Neural computation, 9 (8), 1735-1780 (1997). doi: 10.1162/neco.1997.9.8.1735
  • Sosa, P. M. Twitter sentiment analysis using combined lstm-cnn models (2017).

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