Sarcasm Detection Demo

[NripeshN]

Objective: Create a machine learning model with ivy that can detect sarcasm in text data. Given the nuanced nature of sarcasm, which often relies on the context and tone, this project presents an intriguing challenge in the field of natural language processing (NLP). The goal is to contribute to more sophisticated text analysis tools that can navigate the complexities of human communication.

Task Details:

• Dataset: The project will use the sarcasm dataset available on Kaggle, which you can find here: Sarcasm Dataset. This dataset provides a collection of sentences labeled for the presence or absence of sarcasm, offering a foundation for training and testing the sarcasm detection model.

• Expected Output: Participants are to submit a Jupyter notebook that outlines the sarcasm detection model's development process, including data preprocessing, feature extraction from text, model training, and evaluation. The submission must also include the trained model files.

• Submission Directory: Place your completed Jupyter notebook and associated model files in the Contributor_demos/Sarcasm Detection subdirectory within the unifyai/demos repository.

How to Contribute:

1. Fork the unifyai/demos repository to your GitHub account.
2. Clone the forked repository to your local system.
3. Create a new branch specifically for your work on the Sarcasm Detection demo.
4. Develop your model, carefully documenting your approach and findings in the Jupyter notebook.
5. Store your notebook and model files in the Contributor_demos/Sarcasm Detection directory.
6. Once your work is ready, push your branch to your forked repository.
7. Open a Pull Request (PR) to the unifyai/demos repository with a clear title, such as "Sarcasm Detection Demo Submission".

Contribution Guidelines:

• Ensure your code is well-documented to ease understanding and replication.
• In your PR, provide a summary of your methodology, key insights, and any significant challenges you encountered, offering insights into your development process.