Detecting Sarcasm Text in Sentiment Analysis Using Hybrid Machine Learning Approach

Detecting Sarcasm Text in Sentiment Analysis Using Hybrid Machine Learning Approach

Автор: Neha Singh, Umesh Chandra Jaiswal, Ritu Singh

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 4 vol.16, 2024 года.

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It's getting harder for 21st-century citizens to effectively detect sarcasm using sentiment analysis in a world full of sarcastic people and identifying sarcasm aids in understanding the unpleasant truth hidden beneath polite language. While sarcasm in text is frequently identified, very little research has been done on text sarcasm recognition in memes. This study uses a hybrid machine learning strategy to increase accuracy in identifying sarcasm text in sentiment analysis. It also compares the hybrid approach to existing approaches, like Random Forest, Logistic Regression, Naive Bayes, Stochastic Gradient Descent, and Decision Tree. The effectiveness of several methods is assessed in this study using recall, precision, and f-measure. The results showed that the suggested strategy (0.8004%) received the highest score when the prediction accuracy of several machine learning approaches was compared. The proposed hybrid approach performs much better in terms of enhancing accuracy.

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Sentiment Analysis, Sarcastic People, Machine Learning, Sarcasm Text

Короткий адрес: https://sciup.org/15019378

IDR: 15019378   |   DOI: 10.5815/ijisa.2024.04.05

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