The application has been deployed as an API using heroku. It takes in a statement from the user and returns either of two values. Positive or negative.

Website link: https://sentiment-analysis-api-project.herokuapp.com/docs

• Import the libraries from requirements.txt
  It can be done by writing the following comand in the terminal
  pip install -r requirements.txt
• Run the main.py file to get the model and the tokenizer, one can tune the hyper-parameters as one wishes.
• Open the app.py file and run the following command in the CLI python -m uvicorn app:app --reload after which the application will be hosted on the local system and a URL will be provided
• One can post the provided URL and observe the results on their own

• As this was an NLP task one had to first clean the text and then find a way to embed them
• NLTK and regrex were used to clean the text and end up with the processed text
• The in-built keras tokenizer was used for embedding the words, one-hot encoding, BoW, word2vec were considered but they did not provide the desirable results
• As for the model, a bidirectional lstm model has been built with a dropout layer and a final layer with sigmoid activation for the task of binary classification
• FastAPI was used to make the restful API
• Heroku was used for deployment

• Accuracy -> 0.8939
• Precision -> 0.9007
• Recall -> 0.8874
• F1 score -> 0.8940

• tensorflow-cpu has been put in requirements.txt instead of the traditional tensorflow as tensorflow contains packages for both GPU and CPU which causes the slug size to increase by a factor of lot. While running of local system consider installing tensorflow by running the following command pip install tensorflow if any issues are encountered.
• The slug file of API is quite large, so if the API does not load in the first try. Kindly refresh the page.