Data Mining for Emotional Analysis of Big Data

Abstract

We’re in the midst of the “big data” age right now. Users generate enormous amounts of text data through a variety of means, including social media sites, e-commerce sites, and many kinds of scientific investigations. With this “Text data,” companies may have a better understanding of how the public perceives their brand and use that information to guide future business decisions. As a result, it is imperative for businesses to use sentiment social media data (Big data) to generate forecasts. Open-source big data tools and machine learning techniques are needed to process massive amounts of text data in real time. To this end, we developed a machine learning algorithm-based system for analyzing sentiment in large datasets. Here, the system for text analysis system reviews datasets utilizing the Apache Spark has been built and implemented utilizing the Nave Bayes and Support Vector Machines classification techniques. In addition, accuracy was used to gauge how well the algorithms worked. As demonstrated by these experiments, the Algorithms are quite effective at managing large sentiment datasets. This will be more useful for businesses, governments, & individuals to increase their value. © 2023, Ismail Saritas. All rights reserved.