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Issue Title: New BRICS Member Sentiment Analysis #518
Info about the related issue (Aim of the project) : The main aim was to find a best model which can accurately predict the sentiments of the text based on the BRICS member texts
Name: Tanuj Saxena
Email ID for further communication: tanuj.saxena.rks@gmail.com
Closes: #518 number that will be closed through this PR
Describe the add-ons or changes you've made ๐
So, For the issue 518, it was text dataset with label 0,1 for postive and negative text.
Initially did some pre-processing just normal one then test few model the accuracy was just around 50% then have to do some more advance data pre-processing so that models can predict well. So, there did Data augmentation and other required steps. Then tested with more that 10 models where 6 best model which give 70%+ accuracy where keep it in the file
Type of change โ๏ธ
What sort of change have you made:
[x] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[x] Code style update (formatting, local variables)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update
How Has This Been Tested? โ๏ธ
Describe how it has been tested
The implemented the webapp where i test with the Positive commit from the original dataset to check it correctness
Checklist: โ๏ธ
[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my own code.
[x] I have commented my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[x] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[x] Any dependent changes have been merged and published in downstream modules.
Pull Request for ML-Crate ๐ก
Issue Title: New BRICS Member Sentiment Analysis #518
Closes: #518 number that will be closed through this PR
Describe the add-ons or changes you've made ๐
So, For the issue 518, it was text dataset with label 0,1 for postive and negative text. Initially did some pre-processing just normal one then test few model the accuracy was just around 50% then have to do some more advance data pre-processing so that models can predict well. So, there did Data augmentation and other required steps. Then tested with more that 10 models where 6 best model which give 70%+ accuracy where keep it in the file
Type of change โ๏ธ
What sort of change have you made:
How Has This Been Tested? โ๏ธ
Describe how it has been tested The implemented the webapp where i test with the Positive commit from the original dataset to check it correctness
Checklist: โ๏ธ