LSTM News Categorizer

Our team developed LSTM models using Python to categorize news articles effectively. LSTM, a cutting-edge neural network architecture, was chosen for its capacity to grasp long-term relationships in data sequences, making it ideal for understanding and categorizing news content.

The models were trained to classify news into ten predefined topics, optimizing the user experience by organizing information based on themes such as World, Health, Business, and more. The successful integration of LSTM models elevated the platform's accuracy, resulting in precise news categorization and enhanced user engagement.

This feature seamlessly recognizes the language of each news article, providing the ability to sort and organize the news according to the detected language.

The language detector significantly enhances the versatility of the news aggregator, ensuring that users can effortlessly access news in their preferred languages. This feature optimizes the user experience by offering language-based sorting options, making news consumption even more personalized and accessible.

Building a robust system to collect news articles from diverse sources, process them through the ML models for categorization, and securely store the information in a database. This involved seamless integration with various APIs and services to ensure an extensive news source pool.

The Onix team developed a webview-based interface accessible on Android and iOS apps, ensuring a delightful user experience. The frontend allowed users to view the latest categorized news, customize their preferences, and filter news by category and source.

Integration with the client's existing services was a critical component. The team seamlessly integrated the news aggregator with the client's infrastructure, ensuring a cohesive user journey and enabling user identification within the app.