Sarcasm Detection Model using Machine Learning(NLP)

[KamakshiOjha]

Pull Request for ML-Crate 💡

Issue Title: Sarcasm Detection Model using Machine Learning(NLP)

• Info about the related issue (Aim of the project) : This repository contains various deep learning models for detecting sarcasm in text data using TensorFlow and Keras. Each model explores different architectures and techniques to improve sarcasm detection performance.
• Name: Kamakshi Ojha
• Email ID for further communication: [email protected]
• GitHub ID: https://github.com/KamakshiOjha
• Idenitfy yourself: AI/ML practitioner

Closes: #661

Describe the add-ons or changes you've made 📃

I have added a new folder for sarcasm detection using NLP which includes the following:

• Images: Added Images for the accuracy and loss for each model.
• Dataset: Included the dataset required for training and testing the sarcasm detection models.
• Models: Implemented 7 models for sarcasm detection using different architectures and techniques.
• README.md: Created a comprehensive README file explaining the project, models.
• requirements.txt: Added a requirements file to specify the necessary Python packages for the project.

Type of change ☑️

What sort of change have you made:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• 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? ⚙️

Describe how it has been tested
To test the sarcasm detection models, we used a custom input headline and processed it through a predefined preprocessing function.

Describe how have you verified the changes made
The raw prediction value and the classification (sarcastic or not) are printed to verify the model's performance as output.

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

[github-actions]

Our team will soon review your PR. Thanks @KamakshiOjha :)

[abhisheks008]

Looks good to me. Approved @KamakshiOjha

Need some time to merge this pull request as I haven't gone through the guidelines for adding the points for VSOC. I'll merge this by EOD or by Friday evening.