Social Media Sentiment Analysis

Abstract

In the digital era, social media has become a
powerful platform where users express their
opinions and emotions on various topics such as
politics, brands, products, and current events. This
project, Social Media Sentiment Analysis, aims to
analyze and interpret these user sentiments to
classify them as positive, negative, or neutral using
techniques from Natural Language Processing
(NLP) and Machine Learning (ML).
The system collects data from platforms like
Twitter, Facebook, or Instagram, preprocesses the
textual content to remove noise, and applies
sentiment classification models such as Naïve
Bayes, Support Vector Machines, or deep learning
models like BERT. The analyzed results are
visualized through charts and graphs to help
organizations, researchers, and businesses gain
actionable insights from public opinion. This
project highlights the importance of sentiment
analysis in brand monitoring, crisis detection,
market research, and decision-making.