Framework for sentiment drift analysis in real time twitter data streams

Abstract

Social media has a significant impact on society as users express their emotions and sentiments on these platforms. This impact includes politics, finance, business, and social issues. Twitter is a popular social media platform that allows users to share their thoughts and opinions in short messages known as tweets. As a result, Twitter has become an essential tool for analyzing public sentiment and opinion on various topics. Real time analysis of Twitter data can yield valuable insights into public opinion and sentiment on diverse topics. This information can be highly beneficial for businesses, politicians, and other organizations looking to understand their audience and adjust their strategies accordingly. One of the analytical methods that can be performed on tweets is sentiment analysis. Sentiment analysis is the process of using natural language processing, text analysis, and computational linguistics to identify and extract subjective information from tweet data. Nevertheless, real time sentiment analysis on Twitter faces certain drawbacks. newline