Sarcasm Detection Model using Machine Learning(NLP)

[KamakshiOjha]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Sarcasm Detection Model Comparison :red_circle: Aim : To determine the best-performing machine learning model for sarcasm detection in headlines by comparing multiple algorithms based on accuracy scores. :red_circle: Dataset : https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection :red_circle: Approach : Try to use 3-4 algorithms (such as Embedding + GlobalAveragePooling1D, Embedding + Bidirectional LSTM, Embedding + Conv1D + Bidirectional LSTM) to implement the models. Perform exploratory data analysis (EDA) on the dataset before creating any models to understand its characteristics.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name : Kamakshi Ojha
• GitHub Profile Link : https://github.com/KamakshiOjha
• Approach for this Project : Architecture: Embedding layer, two Conv1D layers (128 and 64 filters), followed by a Bidirectional LSTM layer (32 units) and Dense layers. Purpose: Combines convolutional layers with LSTMs to extract local features and capture temporal dependencies.
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) VSOC

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

Assigned to you @KamakshiOjha Try to implement 6-7 models for this problem statement.

[github-actions[bot]]

Hello @KamakshiOjha! Your issue #661 has been closed. Thank you for your contribution!