This project is aimed at developing a sentiment analysis application utilizing the embedded Watson AI libraries. The application is designed to analyze the sentiment of a provided text and subsequently deploy it as a web application using the Flask framework.
To successfully complete this project, you'll need to accomplish the following eight tasks, building upon the knowledge you've acquired throughout the course:
Begin by cloning the project repository from the provided source. https://github.com/ibm-developer-skills-network/zzrjt-practice-project-emb-ai
Utilize the Watson Natural Language Processing (NLP) library to build a sentiment analysis application. This application should be capable of analyzing the sentiment of the input text.
Format the application's output to present the sentiment analysis results in a clear and user-friendly manner.
Package the sentiment analysis application so that it can be easily deployed and run on various platforms.
Implement unit tests to ensure the correctness and robustness of your application's functionality.
Deploy the sentiment analysis application as a web application using the Flask web framework. This will make it accessible to users via a web interface.
Enhance the application by incorporating effective error handling mechanisms. This ensures a more reliable and user-friendly experience.
Perform static code analysis to identify and address potential code quality and maintainability issues.
By successfully completing these tasks, you will have created a sentiment analysis web application that leverages Watson AI and Flask, making sentiment analysis accessible to users via a web interface. Additionally, the project will be well-structured, thoroughly tested, and equipped with error-handling mechanisms for a more robust user experience.