Text Classification for News Article

Abstract

Abstract: In the era of information overload, effective organization and categorization of news articles are essential for providing users with relevant and timely information. This project focuses on the development and implementation of a text classification system for news articles. The primary goal is to automatically categorize news articles into predefined topics or classes, enhancing the user experience by enabling efficient content discovery and navigation. The project begins with a comprehensive collection of a diverse dataset of news articles spanning various domains such as politics, sports, technology, entertainment, and more. Preprocessing techniques are employed to clean and tokenize the text, followed by feature extraction methods that capture meaningful patterns within the text data. Various machine learning algorithms, including but not limited to, Naive Bayes, Support Vector Machines, and neural networks, are explored and evaluated to determine the optimal model for text classification. To enhance the performance of the classification system, advanced techniques such as word embeddings and transfer learning are investigated. Word embeddings like Word2Vec, Fast Text, or Glove capture semantic relationships between words, improving the model's ability to understand context. Transfer learning, particularly using pre-trained language models like BERT or GPT-3, leverages large-scale language understanding, enabling the model to generalize better even on limited labeled data. The evaluation of the text classification models involves metrics like accuracy, precision, recall, and F1-score, ensuring a comprehensive understanding of their performance across different classes. Hyperparameter tuning and model optimization are conducted to achieve the best possible results.