SOCIAL MEDIA AND GOOGLE REVIEW SENTIMENT ANALYSIS USING VADER AND FLASK FRAMEWORK

Abstract

Social and Google Review Sentiment Analyzer is a web-based application developed using Python and Flask to analyze customer sentiments from online reviews. The system focuses on extracting review data from web sources and Googlerelated platforms, followed by preprocessing and sentiment classification. It utilizes the VADER sentiment analysis model to categorize reviews into positive, negative, and neutral classes based on polarity scores and compound thresholds. The backend architecture is designed using Flask REST APIs, enabling functionalities such as sentiment analysis, historical data tracking, and daily analytics generation. Data persistence is handled through Flask-SQLAlchemy with SQLite as the default database, along with optional support for MySQL integration. The application incorporates automated data collection techniques using Selenium, BeautifulSoup, and Requests to extract relevant textual content while filtering out noise. The frontend is built using HTML, CSS, and JavaScript, with Chart.js providing interactive visualizations such as sentiment distribution and trend analysis. Overall, the project presents a complete end-to-end sentiment analysis pipeline, combining data acquisition, natural language processing, data storage, and visualization to support decision-making and business insights.