Abstract

In an era characterized by the digital revolution and the widespread adoption of social media platforms, analysing user sentiments online has become essential for grasping public opinion, consumer preferences, and emerging trends. This project delves into sentiment analysis within social media, employing web scraping techniques to gather and evaluate data from diverse online platforms. The central aim of this project is to uncover valuable insights and trends by systematically examining user sentiments. With social media's pervasive influence, individuals and organizations now communicate, share information, and express opinions in unprecedented ways. This abundance of user-generated content presents a unique opportunity to access a vast repository of data that can offer meaningful insights. Sentiment analysis, also referred to as opinion mining, provides a means of understanding and categorizing the emotions, attitudes, and opinions expressed in online content. This project showcases the potential of data-driven approaches in making informed decisions and gaining a competitive advantage in today's digital landscape.