THE PATTERN DETECTION OF TEXTUAL FEATURE ANALYSIS AND CLASSIFICATION BY USING MACHINE LEARNING ALGORITHMS

Abstract

Textual data is rapidly increasing in volume, making it difficult for humans to manually analyze and classify it into meaningful categories. Machine learning algorithms have shown great promise in automating this process, allowing for more efficient and accurate analysis of large volumes of text. In this paper, we use machine learning algorithms for pattern detection, textual feature analysis, and classification of textual data. We explore various techniques, such as bag-of-words, TF-IDF technique, word 2Vec and font text model, and compare their performance in transformers. We also experiment with several machine learning algorithms, such as decision trees, support vector machines, and neural networks, to determine which algorithms are best suited for text classification. Our results show that a BERT model has better accuracy than TF-IDF technique and font text model. Transformers can also be used to achieve high levels of accuracy in text classification tasks. We also identify some of the challenges and limitations of using machine learning for text analysis and discuss potential future directions for research in this field.