Enhanced the Accuracy of Text of a Tweet by Detecting Sarcasm using Transfer Learning
Keywords:
Sarcasm, Sentiment Analysis, Twitter, Transfer Learning, Naive Bayes, Entropy based Decision TreeAbstract
A sentence's polarity can be reversed by the use of sarcasm. Despite the widespread usage of sentiment analysis on social media, identifying and analyzing sentiments is still challenging, given the possibility of sarcasm. Even humans find identifying sarcasm in any given text or sentence difficult. Because of this, sarcasm detection with the help of computers becomes an even more significant and challenging task. Several types of research are done regarding sarcasm detection to detect sarcasm in the given text. Unique words are primarily used in sarcasm, and finding the usage of how these individual words are used in tweets or sentences can help detect sarcasm detection. Our research focuses mainly on detecting sarcasm in the texts and creating state-of-the-art model, by combining two models Naïve Bayes, and Decision Tree(entropy-based approach). In our research, we try to combine the methods of the above two approaches to improve the accuracy of sarcasm detection and create a state-of-the-art method for sarcasm detection in texts primarily tweets of the twitter.
Downloads
References
Heredia, B., Prusa, J., & Khoshgoftaar, T. (2017). Exploring the effectiveness of Twitter at polling the United States 2016 presidential election. In Proceedings of the IEEE 3rd International Conference on Collaboration and Internet Computing (pp. 283-290).
Gupta, R., Kumar, J., & Agrawal, H. (2020). A statistical approach for sarcasm detection using Twitter data. In Proceedings of the 4th International Conference on Intelligent Computing and Control Systems (pp. 633-638).
Saha, S., Yadav, J., & Ranjan, P. (2017, July). Proposed approach for sarcasm detection in Twitter. Indian Journal of Science and Technology.
Sharma, S., & Chakravarty, S. (2018). Sarcasm detection in online review text. ICTACT Journal on Soft Computing, 8(3).
Bamman, D., & Smith, N. (2015). Contextualized sarcasm detection on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 9).
Prasad, A. G., Sanjana, S., Bhat, S. M., & Harish, B. (2017). Sentiment analysis for sarcasm detection on streaming short text data. In Proceedings of the 2nd International Conference on Knowledge Engineering and Applications (ICKEA) (pp. 1-5). IEEE.
Kreuz, R., & Caucci, G. (2007). Lexical influences on the perception of sarcasm. In Proceedings of the Workshop on Computational Approaches to Figurative Language (pp. 1-4).
Mukherjee, S., & Bala, P. K. (2017, February). Sarcasm detection in microblogs using naive Bayes and fuzzy clustering. Technology in Science. Elsevier.
Sentamilselvan, K., et al. (2021). Sarcasm detection in Twitter. IOP Conference Series: Materials Science and Engineering, 1055, 012105.
Ahuja, R., & Sharma, S. (2020). A review paper on sarcasm detection. In Proceedings of the 3rd International Conference on Advances and Applications in Artificial Intelligence (pp. 1-5).
Baroiu, A.-C., & Trăușan-Matu, Ș. (2022). Automatic sarcasm detection: Systematic literature review. Information, 13(8), 399. https://doi.org/10.3390/info13080399
Kitchenham, B., & Charters, S. (2007, January). Guidelines for performing systematic literature reviews in software engineering (Vol. 2).
Eke, C. I., Norman, A. A., Shuib, L., & Nweke, H. F. (2020). Sarcasm identification in textual data: Systematic review, research challenges and open directions. Artificial Intelligence Review, 53(6), 4215-4258. https://doi.org/10.1007/s10462-019-09791-8
Genc-Nayebi, N., & Abran, A. (2017). A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software, 125, 207-219. https://doi.org/10.1016/j.jss.2016.11.027
Maynard, D. G., & Greenwood, M. A. (2014). Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In LREC 2014 Proceedings.
Bharti, S. K., Babu, K. S., & Jena, S. K. (2015). Parsing-based sarcasm sentiment recognition in Twitter data. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 1373-1380). https://doi.org/10.1145/2808797.2808910
Veale, T., & Hao, Y. (2010). Detecting ironic intent in creative comparisons. In Proceedings of the ECAI 2010 (pp. 765-770).
Birjali, M., Kasri, M., & BeniHssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134. https://doi.org/10.1016/j.knosys.2021.107134
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731-5780. https://doi.org/10.1007/s10462-022-10144-1
Hatzivassiloglou, V., & McKeown, K. R. (1997). Predicting the semantic orientation of adjectives. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the 8th Conference of the European Chapter of the Association for Computational Linguistics (pp. 174-181). https://doi.org/10.3115/976909.979640
Turney, P. D., & Littman, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21(4), 315-346. https://doi.org/10.1145/944012.944013
Han, H., Zhang, J., Yang, J., Shen, Y., & Zhang, Y. (2018). Generate domain-specific sentiment lexicon for review sentiment analysis. Multimedia Tools and Applications, 77(16), 21265-21280. https://doi.org/10.1007/s11042-017-5529-5
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
