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align=\"center\"\u003eSentinalyzer\u003c/h1\u003e\u003c/br\u003e\n\n\u003cp align=\"center\"\u003e\n:smile: A web app which detects the sentiment of a movie review, whether it is positive or negative. :unamused:\n\u003c/p\u003e\n\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/k4u5h4L\"\u003e\u003cimg alt=\"k4u5h4L GitHub badge\" src=\"https://badgen.net/badge/GitHub/k4u5h4L?icon=github\u0026color=24292e\"/\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n\u003cimg width=\"320px\" src=\"assets/logo.png\" alt=\"Sentinalyzer logo\"\u003e\u003c/img\u003e\n\u003c/p\u003e\u003cbr\u003e\n\n## Technologies used:\n\n- React (create-react-app) for the front end.\n\n- Python-Django for the backend server side.\n\n- Tensorflow 2.3.0 with the Keras syntax for the model training and predictions.\n\n## To run:\n\n- Clone and cd into the repo.\n\n```\ngit clone https://github.com/k4u5h4L/Sentinalyzer.git \u0026\u0026 cd Sentinalyzer\n```\n\n- Cd into the client and install the required dependencies\n\n```\ncd client \u0026\u0026 npm install\n```\n\n- In the `server/form/views.py` file, change the file path of the model and pickle file.\n\n- Install the dependencies from the `requirements.txt` file\n\n```\npip install -r requirements.txt\n```\n\n- Run the Django server.\n\n```\npython manage.py runserver\n```\n\n- In another terminal, run the front end React client.\n\n```\nnpm start\n```\n\n- Now visit the location [localhost:3000](http://localhost:3000) to visit the site.\n\n## Note:\n\n- This project is made as a hobby and is \u003cb\u003eNOT\u003c/b\u003e a commercial application.\n\n- Any contribution is welcome. You can fork and submit a pull request.\n\n- The dataset is got from [kaggle](https://kaggle.com) from [this page](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) by [lakshmi25npathi](https://www.kaggle.com/lakshmi25npathi).\n  Please download the dataset and unzip the csv file in this file path `data/imdb_movie_reviews/IMDB_Dataset.csv` to train the model yourself.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fk4u5h4l%2Fsentinalyzer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fk4u5h4l%2Fsentinalyzer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fk4u5h4l%2Fsentinalyzer/lists"}