Abstract:
This study focuses on the application of natural language processing (NLP) in text classification, specifically for Bengali news headlines. Bengali, like many other languages, has seen increased attention in this field, with a primary focus on categorizing unlabeled news items into categories such as national, international, IT, and others. The growing popularity of Bengali news portals and the accessibility of online news make this a relevant area of research. The proposed technique involves preprocessing steps, including tokenization, removal of numbers, special characters, and stop words, with a manually curated stop-word list to enhance performance. The study emphasizes the importance of stop-word elimination in feature selection. The methodology concentrates on classifying Bengali news headlines into eight distinct categories using machine learning models. Data is collected, preprocessed, and divided into training and testing sets
