The Use of Machine Learning to Analyze the Sentiment for Social Media Networks

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The amount of textual information on the internet has increased significantly with the debut of social media platforms like Twitter, including news stories and historical records. This is due to the growth of Web 2.0. More individuals are using the internet and different forms of social media to share their thoughts and feelings with the world. As a result, more phrases with emotional nuance were created by the general public. It is natural that researchers will look into new approaches to understanding people's emotions and reactions. In addition to providing a novel hybrid system that combines text mining and neural networks for sentiment categorization, this study evaluates the efficacy of many machine learning and deep learning techniques. More than a million tweets from across five different topics were utilized to create this dataset. Seventy-five percent of the dataset was used for training, while the remaining twenty-five percent was used for testing. When compared to traditional supervised learning methods, the system's hybrid approach displays a maximum accuracy of 83.7%.