The objective of this project is to classify news category from articles. The input data consist of 2225 news articles from the BBC news website corresponding to stories in 5 topical areas (e.g., business, entertainment, politics, sport, tech). LSTM has been applied in the classification task to categorize articles.

• TensorFlow 2.0 has been used to train the model.
• Word embedding is used in feature generation.
• TSNE is used to visualize the word vectors in 2d space.
• L1 regularization is applied to prevent overfitting.
• 95% accuracy has been achieved.

This Project’s GitHub Repository

• Classification
• RNN
• NLP
• Embedding