A stacked LSTM to categorize textual news feeds

In this notebook I will experiment with sequential data classification using LSTM. My dataset is comprised of news feed with their categories, the news feeds being a long paragraph of news related to a special topic and topics being either sport, entertaimnent, tech, business or politics. I will need to do some pre-processing of the data including TF-IDF vectorization which maps each paragraph to a long vector comprised of the scores of the words within it. Then I will build an LSTM network that can classify the news to their correspondent category. So let's get started !

• Vectorize the news feeds to be machine learning ready
• Build an LSTM to categorize them

• Importing libraries

• The dataset

• Label encoding the target

• Text pre-processing

• Train/Validation/Test splits

• Creating the dataloaders

• Building the network

• Training the network

• Testing the network

• Inference

• Conclusion

  Libraries

  • Pandas
  • Numpy
  • Torch
  • NLTK
  • Sklearn
  • Scipy

  Data source

  https://www.kaggle.com/datasets/ahmedashrafahmed/text-classification?fbclid=IwAR2oXktbbTlGmtm8-4XBx2T2zahPBIJSq3y-WuJPagkJgxpCchd47xDSLso