Extracting Contextual Feature Form Hinglish Short Text by Handling Spelling Variation at Character and Word Level

Authors

  • Rajshree Singh Research Scholar, School of Computer Application, Babu Banarasi Das University, Lucknow
  • Reena Srivastava Dean, School of Computer Application, Babu Banarasi Das University, Lucknow

Keywords:

Pre-Processing, short text, Auto-Encoder, Hinglish

Abstract

The major purpose of sarcasm detection has been to comprehend the text's evaluation. Sarcasm detection is regarded as one of the most provocations in it, and it has been subject to significant provocation. Irony is a unique manner of expressing information that contradicts a notion and creates confusion. Data pre-processing is one of the main duties performed by most developers. Numerous studies on irony detection use a variety of feature extraction techniques. These studies used a variety of machine learning classification models that includes Naive Bayes, Logistic Regression, etc. Precision, recall, and F-score are among the research project results that can be utilized to forecast the most appropriate model. This study discusses numerous methods for detecting sarcasm and irony in text. Extra Tree Classifier and gradient boosting classifier gives the best result having F-Score 95.43 and 95.29 respectively with Wn = 4,Cn = 3 and CWn =4 to detect sarcasm in Hinglish Language.

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Sarcasm Detection using Machine Learning

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Published

17.05.2023

How to Cite

Singh, R. ., & Srivastava, R. . (2023). Extracting Contextual Feature Form Hinglish Short Text by Handling Spelling Variation at Character and Word Level. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 713–719. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2906

Issue

Vol. 11 No. 6s (2023)

Section

Research Article

License

Copyright (c) 2023 Rajshree Singh, Reena Srivastava

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