[Project Addition] : Sarcasm Detection using NLP

[bristiHalder]

trafficstars

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Sarcasm Detection :red_circle: Aim: 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. :red_circle: Dataset: The models are designed to work with any text classification dataset.

---

📍 Follow the Guidelines to Contribute to the Project:

• You need to create a separate folder named 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 on other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

---

: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 the issue number along with it.
• Follow the Contributing Guidelines & Code of Conduct before starting Contributing.

---

:white_check_mark: To be Mentioned while taking the issue :

• Full name: Bristi Halder
• GitHub Profile Link: https://github.com/bristiHalder
• What is your participant role? GSSoC'24 Contributor

---

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]

Hi @bristiHalder can you please share a brief about the models you want to implement here?

[bristiHalder]

@abhisheks008 Sure sure!

1. Global Average Pooling

• Embedding layer followed by a GlobalAveragePooling1D layer and Dense layers.

2. Stacked Bidirectional LSTM
3. A single Bidirectional LSTM layer with Dense layers
4. Convolutional layers followed by a Bidirectional LSTM
5. CNN-LSTM Hybrid with Batch Normalization
6. GRU layers with Dropout for regularization
7. An attention layer after Bidirectional LSTM.

[abhisheks008]

@abhisheks008 Sure sure!

1. Global Average Pooling

• Embedding layer followed by a GlobalAveragePooling1D layer and Dense layers.

2. Stacked Bidirectional LSTM
3. A single Bidirectional LSTM layer with Dense layers
4. Convolutional layers followed by a Bidirectional LSTM
5. CNN-LSTM Hybrid with Batch Normalization
6. GRU layers with Dropout for regularization
7. An attention layer after Bidirectional LSTM.

Assigned this issue to you @bristiHalder