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This is a project which contains different models ,that tries to do text classification . I am using amazon food reviews dataset here , and the labels are 1)positive 2)neutral 3)negative . I have implemented several models like rule based classifier, naive bayes classifier and many models yet to implement

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text-classification

This is a project which contains different models that are built from stratch without using any inbuilt models and methods , that tries to do text classification . I am using amazon food reviews dataset here , and the labels I am considering for reviews are

1)positive

2)neutral

3)negative.

I have implemented several models like rule based classifier, naive bayes classifier and many models yet to come.

Link to dataset : https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews

The test set accuracy are also mentioned at the the of corresponding notebooks. And note that these are just basic implementations and with many advanced practices , test accuracy can be improved further.

rule based classifier accuracy : around 44%

naive bayes classifier accuracy : around 78%

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This is a project which contains different models ,that tries to do text classification . I am using amazon food reviews dataset here , and the labels are 1)positive 2)neutral 3)negative . I have implemented several models like rule based classifier, naive bayes classifier and many models yet to implement

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