Sarcasm Detection for Cross Domain Applications

[ShraddhaSabde]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Sarcasm Detection For Cross Domain Applications. :red_circle: Aim : Implement Sarcasm Detection in Cross Domain Applications :red_circle: Dataset : :red_circle: Approach : Sarcasm Detection in Cross Domain Applications This project proposes the accuracy and efficiency of ML and NN models trained on one dataset and tested on other dataset. SARC dataset is used for training and amazon review dataset is used for testing the models. This enables Sarcasm detection on Cross Domain applications.

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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 : Shraddha Vikas Sabde
• GitHub Profile Link : https://github.com/ShraddhaSabde
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :This project proposes the accuracy and efficiency of ML and NN models trained on one dataset and tested on other dataset. SARC dataset is used for training and amazon review dataset is used for testing the models. This enables Sarcasm detection on Cross Domain applications.
• What is your participant role? (GSSoC24)

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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]

Finish the previously assigned issue first. @ShraddhaSabde

[Rashigera]

Hi I would like to be a part of it and give it a try as this is a good opportunity for me as a beginner

[abhisheks008]

Hi @Rashigera to work on this issue you need to share the approach for solving this problem which should be solely based on deep learning methods. Also you need to confirm with the dataset that you are going to use here for this problem statement.

[Sweedle24]

Hi @abhisheks008 , I would like to work on this if it isn't already assigned . I'm a beginner and this would be a great opportunity for me. This is the dataset I want to try on Sarcasm on Reddit : https://www.kaggle.com/datasets/danofer/sarcasm Approach

• start with ML models for baseline performance
• use Deep learning models to capture dependencies between textual data

[abhisheks008]

Hi @Sweedle24 thanks for sharing the dataset.

In the approach you mentioned about machine learning models as a baseline, can you mention the models you are planning to use?

Secondly, for the deep learning models, I'd suggest you to implement at least 3-4 models, compare the accuracy scores to find out the best fitted model for this problem statement. Also can you mention the deep learning models/methods too?

[Sweedle24]

Hi @abhisheks008 , Thanks for responding As for machine learning models I'm thinking of starting with Logistic Regression since the dataset is already labelled, then maybe move towards Random Forest for non-linear feature extractions . In-terms pf deep learning given the dataset's size and the complexity of sarcasm detection I'm thinking of using a model like BERT. Please do provide your suggestions on this as I'm a complete beginner to this field.

[abhisheks008]

From the machine learning POV, it's good. But for deep learning models, apart from BERT what other models you are planning to implement here? Need to implement at least 2 more models.

@Sweedle24

[Sweedle24]

Hi @abhisheks008, What other models would you recommend exploring?

[abhisheks008]

RoBERTa, DistilBERT will work as well.

[Sweedle24]

Ok

[Sweedle24]

Hi @abhisheks008 , can I take up this issue?

[abhisheks008]

Hi @Sweedle24 you can start working on it. Before you start can you mention in which open source event you are participating in?

[Sweedle24]

Hi @abhisheks008 I'm a beginner, I wanted to understand and learn more on this domain hence i approached this issue. I'm not participating in any open source event

[abhisheks008]

Cool, go ahead @Sweedle24