This project is an end-to-end Flask web application designed for sentiment analysis, utilizing models created with Scikit-learn and VADER Sentiment.

• Flask
• Scikit-learn
• Requests
• NLTK
• RE
• vaderSentiment
• VADER Sentiment

VADER (Valence Aware Dictionary and Sentiment Reasoner) is a lexicon and rule-based sentiment analysis tool specifically tailored to analyze sentiments expressed in social media and performs well on texts from various domains. For more information about VADER, visit VADER Sentiment.

Sentiment analysis, a crucial field in Natural Language Processing, involves automatically detecting the emotional states conveyed in text. It finds extensive applications in analyzing voice-of-customer materials such as product reviews on e-commerce platforms like Amazon, movie critiques, or social media posts. Sentiment analysis can range from basic polarity classification (positive/negative) to more advanced analyses of sentiment states.

Sentiment analysis enables companies to make informed decisions. For example, if public sentiment towards a product is unfavorable, a company may choose to alter the product or discontinue its production to prevent losses.

With the increasing number of users on social media platforms, websites like Facebook and Twitter have become rich sources for parsing public sentiment.

• Below is a snapshot of the app's output:

This README provides an overview of the Sentiment Analysis Machine Learning Flask App, its technologies, and its significance in understanding and analyzing sentiments in text data.