News Analyser, Aggregator & Translator

Abstract

In recent years, the rapid advancement of technology and widespread use of the Internet have led to the spread and increase of media use. However, this situation also brings with it problems related to the distribution and analysis of media content. A major problem is that the volume of information makes it difficult for users to identify relevant news and understand their opinions. Applications are made according to user instructions and dates. The application uses various types of machine learning to increase the accuracy of information distribution. After evaluating various models, the random forest algorithm was selected because it performed best in dividing news content into predefined groups  Pygooglenews. Users can specify keywords and dates to filter news content. The application also includes features to interpret news from different media and perform sentiment analysis to give the user an idea of the general opinion about an article. Preprocessing, feature extraction and model evaluation. In addition, analysis and interpretation methods as well as all types of web applications are also examined. The study also investigates the effectiveness of machine learning algorithms in improving the accuracy and precision of news classification and analysis. and reliable network capacity. The program provides practical solutions to users who want to better analyze and interpret media content, providing useful tools for understanding topics and thinking about major news online.