This repository consists of files required for end to end implementation and deployment of Machine Learning NLP Restaurant Reviews Sentiment Analysis web application created with flask and deployed on the Heroku platform.
If you want to view the deployed model, click on the following link:
https://resrevsent.herokuapp.com/
A glimpse of the web app:
• If you encounter this webapp as shown in the picture given below, it is occuring just because free dynos for this particular month provided by the Heroku platform have been completely used. You can access the webpage on 1st of the next month.
• Sorry for the inconvenience.
The Restaurant Reviews Sentiment Analysis is a Flask web application which classifies/detects sentiments of customers as positive or negative sentiments. In this ML-NLP model, restaurant reviews dataset from Kaggle (also available in UCI ML Library) was used to perform Sentiment Analysis using Stemming and Bag of Words model to classify reviews into two sentiments — Liked(1) and Disliked(0). Since, the data is fairly balanced, we are only concerned with accuracy_score. From various Classifiers, highest accuracy achieved was 81% with cross_val_score of 79.4%.
The code is written in Python 3.6.10 and it makes use of NLTK library for text processing.
If you don't have Python installed, you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:
pip install -r requirements.txtLogin or signup in order to create virtual app. You can either connect your github profile or download ctl to manually to deploy this project.
The next step would be to follow the instruction given in the Heroku Documentation to deploy a web app.
If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result