Sentiment Analysis of Social Media Presence by using DistilBert

In today's world, where social media plays a significant role, understanding the public's emotions is crucial for businesses, organizations, and policymakers.This study focuses on analyzing the sentiments expressed in social media posts by using a blend of advanced methods, including data collection, text preprocessing, and embedding strategies.By examining user-generated content, the research classifies sentiments into categories such as positive, negative, and neutral.It evaluates the analysis through various metrics like accuracy, precision, and sentiment trends, offering a detailed view of emotional expression.Furthermore, the study tracks how sentiments change over time, identifying shifts and emerging patterns.It also explores how both text and visual elements contribute to sentiment, offering deeper insights into online communication.The study links user engagement indicators, such as likes, shares, and comments, with sentiment types, highlighting the effect of emotions on social media behaviour.By combining quantitative data with qualitative observations, the research uncovers challenges in sentiment analysis and continuously improves the model through user input.This analysis provides businesses and policymakers with actionable insights to better understand and react to public sentiment.