Framework for Text Classification

Text classification is a crucial task in natural language processing (NLP) that aims to assign predefined categories to text based on its content. This study focuses on classifying news articles into specific categories by leveraging various machine learning and deep learning algorithms, including Logistic Regression, Naive Bayes, BERT, RoBERTa, and DistilBERT. These algorithms are applied to identify the relevant category for each news article, based on its textual features. The project employs traditional machine learning techniques such as Logistic Regression and Naive Bayes alongside advanced transformer-based models like BERT, RoBERTa, and DistilBERT to capture semantic nuances in the text and improve classification accuracy. The model training and evaluation are carried out using Python, with libraries such as scikit-learn for traditional models and Hugging Face’s Transformers for deep learning-based approaches. The system aims to provide an efficient and scalable solution for automatic news categorization, which can be extended to various domains like sentiment analysis, topic modeling, and content recommendation. By incorporating both traditional and state-of-the-art techniques, the project highlights the evolving landscape of text classification and its practical applications in real-time news categorization.

Keywords: Text Classification, Logistic Regression, Naive Bayes, BERT, RoBERTa, DistilBERT, News Categorization, Machine Learning, Deep Learning, Natural Language Processing (NLP), Sentiment Analysis, Topic Modeling, Hugging Face, Python.