Sentiment Analysis for Restaurant Reviews (NLP)

[ghousiya47]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Sentiment Analysis for Restaurant Reviews (NLP) :red_circle: Aim : Perform EDA and predict the review, positive or negative :red_circle: Dataset : https://www.kaggle.com/datasets/d4rklucif3r/restaurant-reviews :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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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 :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

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Happy Contributing 🚀

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

[ghousiya47]

@abhisheks008 Sir I have a created a PR, can you please assign me under JWOC,. I need to create a classification model.

[abhisheks008]

This project repo is solely based on machine learning methods, instead of NLP methods (For those models we have Deep Learning Simplified Repository). Can you brief the issue w.r.t. ML-Crate.

[ghousiya47]

@abhisheks008 here we need to create a classification model to predict whether the review is positive or negative using various ml algorithms like naive bayes, random forest algorithm, logistic regression. nlp is because the review is in our natural language, like review it was fantastic. so we need to first clean our review data by removing stopwords, punctuation, perform eda, then vectorize our review, andd then create a ml model.

[ghousiya47]

@abhisheks008 before we create ml model we need to do tokenization of review column

[abhisheks008]

Issue assigned under JWOC 2024 @ghousiya47