Sentiment Analysis Web Application Introduction The Sentiment Analysis Web Application is a Python-based project that uses machine learning and natural language processing (NLP) techniques to determine the sentiment of textual data. This application provides a user-friendly web interface to analyze the sentiment of user-input text, classifying it as positive, negative, or neutral.

The purpose of this project is to demonstrate the application of NLP and machine learning in sentiment analysis, helping users gain insights into the emotional tone of the text.

Features Real-Time Sentiment Analysis: Classifies text as positive, negative, or neutral instantly. User Authentication: Allows users to sign up and log in to use the sentiment analysis tool. Text Input: Accepts text input from users for sentiment analysis. Sentiment History: Provides a dashboard displaying the history of analyzed texts. Interactive UI: Built with Flask and Bootstrap for a responsive, user-friendly interface. Technologies Used Python: The core programming language for backend processing. Flask: A lightweight web framework for developing the web application. Pandas and Numpy: For data manipulation and numerical operations. Scikit-Learn: For machine learning model training and prediction. NLTK (Natural Language Toolkit): For text processing and feature extraction. SQLite: For the database management. Bootstrap: For front-end styling and responsive design.