Enhanced Hybrid Feature Selection Technique for Sentiment Classification

Authors

  • Pankaj Kumar Gautam,Akhilesh A. Waoo

Keywords:

feature selection; sentiment analysis; pre-processing; hybrid feature selection method; one-way ANOVA; recursive feature elimination (RFE).

Abstract

Redundant, irrelevant, and high-dimensional features give inaccurate results in SA. Feature selection (FS) is animportant step in the SA preprocessing process that improves classification accuracy, precision, recall, and F1 score by reducing feature dimensionality and overcomin

References

Alassaf M, Ali MQ. Improving sentiment analysis of Arabic tweets by one-way ANOVA. Journal of King Saud University Computer and Information Sciences. 2022; 34, no. 6: 2849–2859.

Yuce BE, Peter VN, Pawel W. The use of Taguchi, ANOVA, and GRA methods to optimize CFD analyses of ventilation performance in buildings. Building and Environment. 2022; 225: 109587.

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Published

2024-01-15

How to Cite

Pankaj Kumar Gautam,Akhilesh A. Waoo. (2024). Enhanced Hybrid Feature Selection Technique for Sentiment Classification . Journal of Computational Analysis and Applications (JoCAAA), 32(1), 1271–1284. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4566

Issue

Vol. 32 No. 1 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.