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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

A fusion approach to detect sarcasm using NLTK models BERT and XG Boost

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pp. 981–990Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1621XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1621
Pages:
981–990

Abstract

The context-dependence of sarcasm recognition in textual data makes it a challenging problem. This article describes a fusion technique that combines the XGBoost gradient boosting algorithm with BERT embeddings for sarcasm detection. Using this method, textual input is transformed into rich contextual embeddings that are then used to train an XGBoost classifier. The resulting hybrid model is evaluated and trained across multiple datasets, demonstrating its ability to distinguish between text that is sarcastic and non-sarcastic. The results of the experiment indicate that the accuracy matrix performs better than individual models. A comprehensive word cloud analysis that identifies important phrases and language patterns associated with sarcasm is also included in this paper. This study combines the benefits of BERT and XGBoost to advance sarcasm detection techniques.

Keywords

Subject Classifications

Primary 68T50Secondary 03B65

References

[1] Sharma, Surbhi, and Nisheeth Joshi. “An Optimized Approach for Sarcasm Detection Using Machine Learning Classifier.” Data Science and Applications, Springer Nature Singapore, pp. 73–86 (2024).  
[2] Sharma, Satyajeet, and Bhavna Sharma. “Recognize Corrupted Data Packeted While Transferring Data Through Ensemble Machine Learning Techniques.” Journal of Information and Optimization Sciences, vol. 44, no. 7, Taru Publications, pp. 1459–69 (2023). 
[3] Sharma, Surbhi, and Mani Butwall. “An Analytical Approach for Twitter Sarcasm Detection Using LSTM and RNN.” Algorithms for Intelligent Systems, Springer Nature Singapore, pp. 227–36 (2023). 
[4] Sapkal, Vinod, et al. “Deep Learning Based Phishing Website Identification System Using CNN-LSTM Classifier.” Journal of Information and Optimization Sciences, vol. 44, no. 3, Taru Publications, pp. 315–30 (2023). 
[5] Jain, S., Kumar, S., Sharma, V. K., & Poonia, R. C. Peregrine preying pattern based differential evolution for robot path planning. Journal of Interdisciplinary Mathematics, 23(2), 555–562 ((2020)). 
[6] Abdulghani, Farah A., and Nada A. Z. Abdullah. “Hybrid Deep Learning Model for Arabic Text Classification Based on Mutual Information.” Journal of Information and Optimization Sciences, vol. 43, no. 8, Taru Publications, pp. 1901–08 Nov. (2022). 
[7] Govindan, Vithyatheri, and Vimala Balakrishnan. “A Machine Learning Approach in Analysing the Effect of Hyperboles Using Negative Sentiment Tweets for Sarcasm Detection.” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, Elsevier BV, pp. 5110–20 Sept. (2022). 
[8] Bharti, Santosh Kumar, et al. “Hyperbolic Feature-based Sarcasm Detection in Telugu Conversation Sentences.” Journal of Intelligent Systems, vol. 30, no. 1, Walter de Gruyter GmbH, pp. 73–89 July (2020). 
[9] Chatterjee, Niladri, et al. “Sarcasm Detection Using Deep Learning-Based Techniques.” Algorithms for Intelligent Systems, Springer Singapore, pp. 237–58 (2020). 
[10] Abulaish, Muhammad, and Ashraf Kamal. “Self-Deprecating Sarcasm Detection: An Amalgamation of Rule-Based and Machine Learning Approach.” 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), IEEE, Dec. (2018). 
[11] Bharti, Santosh Kumar, et al. “Sarcasm Analysis on Twitter Data Using Machine Learning Approaches.” Lecture Notes in Social Networks, Springer International Publishing, pp. 51–76 (2017).

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