https://github.com/k4u5h4l/sentinalyzer
A web app which detects the sentiment of a movie review, whether it is positive or negative.
https://github.com/k4u5h4l/sentinalyzer
django javascript jupyter-notebook keras-tensorflow neural-networks python react sentiment-analysis tensorflow tensorflow2
Last synced: 3 months ago
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A web app which detects the sentiment of a movie review, whether it is positive or negative.
- Host: GitHub
- URL: https://github.com/k4u5h4l/sentinalyzer
- Owner: k4u5h4L
- Created: 2020-09-18T05:24:12.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-20T10:42:28.000Z (almost 6 years ago)
- Last Synced: 2025-02-24T06:17:04.060Z (over 1 year ago)
- Topics: django, javascript, jupyter-notebook, keras-tensorflow, neural-networks, python, react, sentiment-analysis, tensorflow, tensorflow2
- Language: Jupyter Notebook
- Homepage:
- Size: 6.11 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Sentinalyzer
:smile: A web app which detects the sentiment of a movie review, whether it is positive or negative. :unamused:
## Technologies used:
- React (create-react-app) for the front end.
- Python-Django for the backend server side.
- Tensorflow 2.3.0 with the Keras syntax for the model training and predictions.
## To run:
- Clone and cd into the repo.
```
git clone https://github.com/k4u5h4L/Sentinalyzer.git && cd Sentinalyzer
```
- Cd into the client and install the required dependencies
```
cd client && npm install
```
- In the `server/form/views.py` file, change the file path of the model and pickle file.
- Install the dependencies from the `requirements.txt` file
```
pip install -r requirements.txt
```
- Run the Django server.
```
python manage.py runserver
```
- In another terminal, run the front end React client.
```
npm start
```
- Now visit the location [localhost:3000](http://localhost:3000) to visit the site.
## Note:
- This project is made as a hobby and is NOT a commercial application.
- Any contribution is welcome. You can fork and submit a pull request.
- 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).
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.