Click here for the Drive link to find the csv files, PPT for both rounds and the APK for the mobile app of Pulse - X
- Python 3.6 or above
- Node JS v12.0.0 or above
- Yarn 1.22.4
- Expo SDK 38
- pip 3 v20.0.x or above
- Clone this repository into your local machine by using
git clone https://github.com/tanmaypardeshi/Pasckathon_git_commit.git
- Install virtualenv using pip3 install virtualenv
- Create a new virtualenv using virtualenv venv
- Activate virtualenv using source venv/bin/activate
- Install all required dependencies using pip install -r requirements.txt
- cd into the backend directory and run the following commands to set up database and populate data into it
- python manage.py makemigrations user
- python manage.py makemigrations employee
- python manage.py migrate
- python manage.py loaddata user.json
- python manage.py loaddata review.json
- To run the development server python manage.py runserver.
- Deactivate the virtualenv using deactivate.
Note: Step 1,2,4 and 5 are one time process to set up data and dependencies.
- cd into the frontend directory.
- Use command yarn to install node modules for the front end.
- Keep the django server up and running to make the website functional.
- Once the node modules have been installed, run yarn start to start the development server.
- cd into the mobile directory.
- Use command yarn to install node modules for the mobile app.
- Keep the django server up and running on your local network to make the app functional. Note that the command for the django server in this case will be python manage.py runserver :8000
- Once the node modules have been installed, run yarn start to start the expo server.
- Scan the QR code on your mobile device using the expo app available on play store.
Directory Name | Description |
---|---|
backend | Django REST framework app with the APIs |
frontend | Front end for the web app made in React |
mobile | Mobile app using React Native and Expo SDK |
Machine_Learning | Notebooks used for the multilayered filtering approach |
scripts | Python scripts to extract data from csv |
Notebook Name | Description |
---|---|
Json_Processing.ipynb | Extracts the data from json format and converts it to a csv |
Text_Preprocessing.ipynb | Preprocesses the data by performing lemmatisation, tokenisation and removes stop words to bring the data in the required format by the models |
SentimentAnalysis.ipynb | The polarity of a tweet is analysed by using a pretrained model from TextBlob |
KeywordsExtraction.ipynb | Extraction of aspects and keywords essential to the product |
SarcasmDetection.ipynb | To detect the true intent behind the review |
NER_BERT.ipynb | To detect the product by fine-tuning BERT |
NER_KeywordExtraction.ipynb | To detect the product by using the DeepPavlov Library and the keywords using TextRank Algorithm |
BertSentimentAna.ipynb | Sentiment analysis module |
Helpfulness.ipynb | Measures helpfulness of review |
ProfanityDetection.ipynb | Checks if the reviews are abusive or not |