Model Deployment

1. Flask or Streamlit App Development: Develop a Flask or Streamlit web application that takes user input in the form of a review and generates the sentiment (positive or negative) of the review.
2. Model Integration: Integrate the trained sentiment classification model into the Flask or Streamlit app for real-time inference.
3. Deployment: Deploy the Flask or Streamlit app on an AWS EC2 instance to make it accessible over the internet.

Workflow

1. Data Loading and Analysis: Gain insights into product features that contribute to customer satisfaction or dissatisfaction.
2. Data Cleaning: Preprocess the review text by removing noise and normalizing the text.
3. Text Embedding: Experiment with different text embedding techniques to represent the review text as numerical vectors.
4. Model Training: Train machine learning and deep learning models on the embedded text data to classify sentiment.
5. Model Evaluation: Evaluate the performance of the trained models using the F1-Score metric.
6. Flask or Streamlit App Development: Develop a Flask or Streamlit web application for sentiment analysis of user-provided reviews.
7. Model Deployment: Deploy the trained sentiment classification model along with the Flask or Streamlit app on an AWS EC2 instance.
8. Testing and Monitoring: Test the deployed application and monitor its performance for any issues or errors.