• Dr. Akhilesh A. Waoo
• Pooja Lodhi

Text classification plays an important role in a variety of tasks, including sentiment analysis, topic ranking, and spam detection. Traditional text classification methods often rely on a single model, such as augmentative machine learning (SVMs) or neural networks. However, this approach may struggle to capture the complex nature of natural language statistics. In recent years, hybrid models combining different categories or techniques have emerged as promising approaches to document classification. This article provides a comprehensive overview of previous developments in text classification using hybrid models. We discuss the motivations behind the use of hybrid models, general modeling, and their strengths and weaknesses. Additionally, we examine various hybrid modeling approaches in text classification, including sentiment analysis, topic classification, and text classification. Finally, we discuss research directions and questions that can be asked in the context of document classification using hybrid models. Overall, this review provides important insights into the latest techniques and applications of mixed media in the text category.

Hybrid approach, Random Forest Classifier, Logistic Regression, Pipeline, Textual and Non-textual datasets.

"A Hybrid Approach to Text Classification: Combining Textual and Non-Textual Features for Improved Accuracy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.b160-b165, June-2024, Available :http://www.jetir.org/papers/JETIR2406122.pdf

"A Hybrid Approach to Text Classification: Combining Textual and Non-Textual Features for Improved Accuracy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppb160-b165, June-2024, Available at : http://www.jetir.org/papers/JETIR2406122.pdf