https://github.com/blackcipher101/newsclassifier

It classifies News into its genre using NLP
https://github.com/blackcipher101/newsclassifier

classification news nlp tensowflow

Last synced: 21 days ago
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It classifies News into its genre using NLP

• Host: GitHub
• URL: https://github.com/blackcipher101/newsclassifier
• Owner: Blackcipher101
• Created: 2020-12-02T10:58:15.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2021-01-10T08:19:22.000Z (almost 4 years ago)
• Last Synced: 2024-10-27T23:58:22.549Z (2 months ago)
• Topics: classification, news, nlp, tensowflow
• Language: Jupyter Notebook
• Homepage:
• Size: 208 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: newsclassfier.ipynb

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README

        
# NewsClassfier

## Thought process :thinking:

I had to do something on my own to brush up what I had learned in the udacity course so I was searching for ideas I was by struck how news papers:newspaper:

classify its content into diffrent sections and if it could be automated 	:wrench: using NLP.

## Hunt for the dataset

I had now started the hunt for the dataset and after 2 days searching I had found nothing but I had eye on dataset provided on kaggle by BBC but it was for text

summary but I had a idea how to tweak it to get it my way. So decided not to waste more time on finding the dataset.

## Preprocessing

Now here comes the hard part :fearful: . I had the dataset already spilt into its section but now I had to load it into an array but not all of them had same number of files and some files were only readble in binary encoding.

After this I had to go through the normal procedure of making them into a single line tokennize it , put paddings in place and split it into traning and valdataion set.

## Model

I actually started out with a embedding layer and two bidierctional LSTMs and it did what all my models do the first the first time they over fit :slightly_frowning_face:.

Then I made it one Bidirectional LSTMs still over fit then I had to move to GlobalAveragePooling1D layer to fix it it gave me an accuracy of 82% which I was happy :smile: with.