RESEARCH OF TEXT CLASSIFICATION BASED ON RANDOM FOREST ALGORITHM

Abstract

Text classification is a fundamental task in Natural Language Processing (NLP) that involves assigning predefined categories to textual data. With the rapid growth of digital content such as emails, social media posts, news articles, and reviews, efficient and accurate text classification has become essential for information organization and retrieval. Traditional methods often rely on manual feature extraction and simple classifiers, which may not effectively handle large-scale and high-dimensional text data. This study focuses on the application of the Random Forest algorithm for text classification, providing a robust and scalable solution for handling complex datasets. The proposed approach involves preprocessing textual data using standard NLP techniques such as tokenization, stop word removal, stemming, and vectorization methods like TF-IDF to convert text into numerical representations. The Random Forest algorithm, an ensemble learning method, is then applied to classify the processed text data. It constructs multiple decision trees and combines their outputs to improve classification accuracy and reduce overfitting. The methodology also includes feature selection and model optimization to enhance performance. Experimental results demonstrate that Random Forest achieves high accuracy and robustness in text classification tasks compared to traditional algorithms such as Naïve Bayes and Support Vector Machines (SVM). The model performs well in handling noisy and unstructured data while maintaining good generalization. However, it may require higher computational resources for large datasets. Overall, this study highlights the effectiveness of Random Forest in text classification and its applicability in domains such as spam detection, sentiment analysis, and document categorization.