Machine Learning-Based Text Categorization With Bag of Words

Abstract

Text categorization, a fundamental task in natural language processing (NLP), plays a pivotal role in organizing and managing the ever-expanding volume of textual data across various domains. This research explores the machine learning applications techniques, with a specific emphasis on the Bag-of-Words (BOW) model, to automate the categorization of text documents. The BOW model is a straightforward yet effective representation method that transforms text data into numerical vectors, disregarding the word order and focusing solely on word frequency. BOW approach is to deal with the representation of text that can be used to any sort of the organization of text. This method is based on BOW concept, which measures the material available on Wikipedia, Gmail, Kaggle (https://www.kaggle.com/datasets), and other sites. The suggested approach is employed to create a Vector Space Representation, subsequently employed to educate a Support Vector Machine categorizer. The purpose is to arrange and gather document records from openly accessible datasets through social networking. The textual outcomes exhibit the contrast between the unprocessed data and the purified data exhibited on the word cloud.” © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.