Turning machine learning models into actual applications other people can use is not something that is covered in most AI and Machine Learning Tutorials. In this article, we are going to create an end-to-end AI Sentiment Analysis web application using Gradio and hugging face transformers. According to Wikipedia, Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. In simple words, Sentiment Analysis is the ability of Artificial Intelligence to analyze a sentence or block of text and get the emotions behind that sentence or block of text. Gradio is an open-source python library that allows you to quickly create easy-to-use, customizable UI components for your ML model, any API, or any arbitrary function in just a few lines of code. Gradio makes it very easy for you to build Graphical User Interfaces and deploy machine learning models.

This week, I'm writing a series of articles about sentiment analysis, which is often referred to as Emotional AI. Engineers of this new brand of technology claim to be able to detect and analyze emotion using electronic sensors and machine learning. To date, media coverage of this emerging field of has been rather credulous, accepting Silicon Valley's assertions about Emotional AI at face value. In this series, I'm attempting to balance that fawning coverage with critical questions, building toward suggestions for ways in which sentiment analysis can be more meaningfully employed by businesses that sincerely wish to enhance their emotional connection with the human beings they serve. This is the fourth article in the series, which also includes the following: Can AI Understand Your Emotions?