Event Categorization from News Articles Using Machine Learning Techniques

Event detection from news articles using machine learning techniques is a crucial task which seeks to automatically identify and extract noteworthy events reported in a collection of news articles. This process involves the application of machine learning algorithms to analyze the textual content and discover relevant events. In this context, two common feature extraction methods - Count Vectorizer and TF-IDF (Term Frequency-Inverse Document Frequency) - play essential roles in transforming the raw text data into numerical representations that can be utilized by machine learning models. These feature vectors are then used as input to train a machine learning model, such as Logistic Regression, Gaussian Naïve Bayes, Random Forest, Multinomial Naïve Bayes, K-NN, Decision Tree, and Support Vector Classifier. Among all the models, Random Forest provides best accuracy of.