Document Type : Articles
Authors
1 Research Scholar
2 Professor
Abstract
Natural languages usually contain context, which is difficult for a machine to understand. Sentiment analysis is a contextual mining technique often used in NLP to identify, understand and extract subjective information in texts, such as people’s comments, feedback, reviews, and opinions. Sentiment analysis is a useful tool for finding the polarity of a sentence. Sarcasm detection is one of the complex areas of sentiment analysis. Sarcasm flips the polarity of the sentence identified by sentiment analysis. Thus, sentiment analysis results may get biased if people use sarcasm in their text. Hence, to understand the sentence's real meaning, we proposed a system of sarcasm detection on tweets using an ensemble approach. We performed sarcasm detection with and without #sarcasm. After training a model and observing earlier studies, We found that the presence of #sarcasm gives a better result. Therefore the author tried implementing a model where #sarcasm is removed from the tweets, and the model is trained. After comparing both models' presence and absence of hashtags, it is found that the lack of the hashtag model works well, which can be used on any plain text without any clue of sarcasm.https://dorl.net/dor/20.1001.1.20088302.2022.20.4.1.0
Keywords
- Bagate, R. A., &Suguna, R. (2019, September). Different Approaches in Sarcasm Detection: A Survey. In International Conference on Intelligent Data Communication Technologies and Internet of Things (pp. 425-433). Springer, Cham.
- Tarigan, J., &Girsang, A. S. (2018). Word similarity score as augmented feature in sarcasm detection using deep learning. Int. J. Adv. Comput. Res, 8(39), 354-363.
- Ghosh, D., Fabbri, A. R., &Muresan, S. (2017). The role of conversation context for sarcasm detection in online interactions. arXiv preprint arXiv:1707.06226.
- Khodak, M., Saunshi, N., &Vodrahalli, K. (2017). A large self-annotated corpus for sarcasm. arXiv preprint arXiv:1704.05579.
- Guo, N., & Shah, R. Finding Sarcasm in Reddit Postings: A Deep Learning Approach.
- Gonzalez-Ibanez, R., Muresan, S., &Wacholder, N. (2011, June). Identifying sarcasm in Twitter: a closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 581-586).
- Reyes, A., & Rosso, P. (2012). Making objective decisions from subjective data: Detecting irony in customer reviews. Decision support systems, 53(4), 754-760.
- Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., & Huang, R. (2013, October). 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).
- Buschmeier, K., Cimiano, P., & Klinger, R. (2014, June). An impact analysis of features in a classification approach to irony detection in product reviews. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 42-49).
- Bagate, R., Saini, A., Sethi, K., Tomar, H., & Singh, A. (2021). Sarcasm Detection and Explainable AI: A Survey. Available at SSRN 3911955.
- Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
- Joshi, A., Tripathi, V., Patel, K., Bhattacharyya, P., & Carman, M. (2016). Are word embedding-based features useful for sarcasm detection?. arXiv preprint arXiv:1610.00883.
- LeCun, Y., Bottou, L., Bengio, Y., &Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
- Poria, S., Cambria, E., Hazarika, D., &Vij, P. (2016). A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815.
- RupaliBagate, R. Suguna (2021). Sarcasm detection of tweets without #sarcasm: data science approach.
- Liebrecht, C. C., Kunneman, F. A., & van Den Bosch, A. P. J. (2013). The perfect solution for detecting sarcasm in tweets# not.
- Gonzalez-Ibanez, R., Muresan, S., &Wacholder, N. (2011, June). Identifying sarcasm in Twitter: a closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 581-586).
- Jamil, R., Ashraf, I., Rustam, F., Saad, E., Mehmood, A., & Choi, G. S. (2021). Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model. PeerJ Computer Science, 7, e645.
- Sonawane, S. S., &Kolhe, S. R. (2020). TCSD: Term Co-occurrence Based Sarcasm Detection from Twitter Trends. Procedia Computer Science, 167, 830-839.