Sarcasm Discernment on Social Media Platform

Namasani Sagarika; Bommadi Sreenija Reddy; Vanka Varshitha; Kodavati Geetanjali; N V Ganapathi Raju; Latha Kunaparaju

doi:10.1051/e3sconf/202130901037

All issues

Volume 309 (2021)

E3S Web Conf., 309 (2021) 01037

References

Open Access

Issue		E3S Web Conf. Volume 309, 2021 3^rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)


Article Number		01037
Number of page(s)		6
DOI		https://doi.org/10.1051/e3sconf/202130901037
Published online		07 October 2021

McKinney, Wes. Python for data analysis, O’Reilly Media, Inc.”, (2012). [Google Scholar]
Bird, Steven, Ewan Klein, and Edward Loper. Natural language processing with Python. “O’Reilly Media, Inc.”, (2009). [Google Scholar]
Li Deng, Yang Liu, Deep Learning in Natural Language Procesing, publisher: Springer [Google Scholar]
Aurélien Géron, Hamds-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 1st Edition [Google Scholar]
Ghosh, Debanjan, Weiwei Guo, and Smaranda Muresan. “Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words.” proceedings of the 2015 conference on empirical methods in natural language processing. (2015). [Google Scholar]
Rajadesingan, Ashwin, Reza Zafarani, and Huan Liu. “Sarcasm detection on twitter: A behavioral modeling approach.” Proceedings of the eighth ACM international conference on web search and data mining. (2015). [Google Scholar]
Liu, Peng, et al. “Sarcasm detection in social media based on imbalanced classification.” International Conference on Web-Age Information Management. Springer, Cham, (2014). [Google Scholar]
Xue, Bai, Chen Fu, and Zhan Shaobin. “A study on sentiment computing and classification of sina weibo with word2vec.” (2014). [Google Scholar]
Davidov, Dmitry, Oren Tsur, and Ari Rappoport. “SSRA.” Proceedings of the fourteenth conference on computational natural language learning. (2010). [Google Scholar]
Berkowitz, Dan, and David Asa Schwartz. “Miley, CNN and The Onion” 10.1 (2016). [Google Scholar]
Lukin, Stephanie M., et al. “Argument strength is in the eye of the beholder: Audience effects in persuasion.” arXiv preprint arXiv:1708.09085 (2017). [Google Scholar]
Bouazizi, Mondher, and Tomoaki Otsuki Ohtsuki. “Pattern based approach.” IEEE Access 4 (2016). [Google Scholar]
Bharti, Santosh Kumar, Korra Sathya Babu, and Sanjay Kumar Jena. Sentiment recognition in twitter data. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, (2015). [Google Scholar]
González-Ibánez, Roberto, Smaranda Muresan, and Nina Wacholder. “Identifying sarcasm in twitter: a closer look.” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. (2011). [Google Scholar]
Pozzi, Federico Alberto, Elisabetta Fersini, and Enza Messina. “Bayesian model averaging and model selection for polarity classification.” International Conference on Application of Natural Language to Information Systems. Springer, Berlin, Heidelberg, (2013). [Google Scholar]
Dhanalaxmi, B., Apparao Naidu, G., Anuradha, K. Adaptive PSO based association rule mining technique for software defect classification using ANN, Procedia Computer Science (2015) [Google Scholar]
Kora, P., Kalva, S.R. Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch, Block, SpringerPlus (2015) [Google Scholar]
Kumar, P., Singhal, A., Mehta, S., Mittal, A. Real-time moving object detection algorithm on high-resolution videos using GPUs, Journal of Real-Time Image Processing (2016) [PubMed] [Google Scholar]
Raju, NV Ganapathi, Machine learning based power saving mechanism for fridge: An experimental study using GISMO III board. Materials Today: Proceedings (2020) [Google Scholar]

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[1] McKinney, Wes. Python for data analysis, O’Reilly Media, Inc.”, (2012). [Google Scholar]

[2] Bird, Steven, Ewan Klein, and Edward Loper. Natural language processing with Python. “O’Reilly Media, Inc.”, (2009). [Google Scholar]

[3] Li Deng, Yang Liu, Deep Learning in Natural Language Procesing, publisher: Springer [Google Scholar]

[4] Aurélien Géron, Hamds-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 1st Edition [Google Scholar]

[5] Ghosh, Debanjan, Weiwei Guo, and Smaranda Muresan. “Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words.” proceedings of the 2015 conference on empirical methods in natural language processing. (2015). [Google Scholar]

[6] Rajadesingan, Ashwin, Reza Zafarani, and Huan Liu. “Sarcasm detection on twitter: A behavioral modeling approach.” Proceedings of the eighth ACM international conference on web search and data mining. (2015). [Google Scholar]

[7] Liu, Peng, et al. “Sarcasm detection in social media based on imbalanced classification.” International Conference on Web-Age Information Management. Springer, Cham, (2014). [Google Scholar]

[8] Xue, Bai, Chen Fu, and Zhan Shaobin. “A study on sentiment computing and classification of sina weibo with word2vec.” (2014). [Google Scholar]

[9] Davidov, Dmitry, Oren Tsur, and Ari Rappoport. “SSRA.” Proceedings of the fourteenth conference on computational natural language learning. (2010). [Google Scholar]

[10] Berkowitz, Dan, and David Asa Schwartz. “Miley, CNN and The Onion” 10.1 (2016). [Google Scholar]

[11] Lukin, Stephanie M., et al. “Argument strength is in the eye of the beholder: Audience effects in persuasion.” arXiv preprint arXiv:1708.09085 (2017). [Google Scholar]

[12] Bouazizi, Mondher, and Tomoaki Otsuki Ohtsuki. “Pattern based approach.” IEEE Access 4 (2016). [Google Scholar]

[13] Bharti, Santosh Kumar, Korra Sathya Babu, and Sanjay Kumar Jena. Sentiment recognition in twitter data. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, (2015). [Google Scholar]

[14] González-Ibánez, Roberto, Smaranda Muresan, and Nina Wacholder. “Identifying sarcasm in twitter: a closer look.” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. (2011). [Google Scholar]

[15] Pozzi, Federico Alberto, Elisabetta Fersini, and Enza Messina. “Bayesian model averaging and model selection for polarity classification.” International Conference on Application of Natural Language to Information Systems. Springer, Berlin, Heidelberg, (2013). [Google Scholar]

[16] Dhanalaxmi, B., Apparao Naidu, G., Anuradha, K. Adaptive PSO based association rule mining technique for software defect classification using ANN, Procedia Computer Science (2015) [Google Scholar]

[17] Kora, P., Kalva, S.R. Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch, Block, SpringerPlus (2015) [Google Scholar]

[18] Kumar, P., Singhal, A., Mehta, S., Mittal, A. Real-time moving object detection algorithm on high-resolution videos using GPUs, Journal of Real-Time Image Processing (2016) [PubMed] [Google Scholar]

[19] Raju, NV Ganapathi, Machine learning based power saving mechanism for fridge: An experimental study using GISMO III board. Materials Today: Proceedings (2020) [Google Scholar]