Applications of Pipelining With ML to Authenticate Emotions in Textual Contents

Abstract

This research chapter aims to provide a smart approach for Human - Machine Interaction development using emotion detection on textual content. These texts can be anything like reviews, tweets, and any form of passage. As the machine is being advanced so that all the performance and commands are given in the text form. This is necessary to analyze the textual content for getting better performance and making the machines smarter. As the customers share their views on social media through the reviews, this mechanism is now spread across all the organization. Nowadays, the number of reviews and tweets are increasing and there is a necessity to analyze the data for further results. In this research, the team analyzes the tweets content in the forms of emotions in which there are multiple forms of the emotions. The machine learning approach is used with tf-idf vectorization for more accuracy. In the presented research, the team performs four machine learning algorithms for analysis; these include Naive Bayes and support vector machine.