Deep Learning Techniques for Sentiment Analysis on Social Media Text

Abstract

Social media enables individuals to express their opinions on a range of contacts with the outside world and topics that interest them. Many people share their thoughts or opinions through text, images, audio, and video. As a result, networking platforms cause a significant volume of unstructured data to be produced on the Internet. Sentiment analysis is a tool that can be used to quickly evaluate data in order to understand human psychology. When compared to current feature-based techniques, deep learning models for sentiment analysis offer detailed representation capabilities and improved performance. The automatic process of sentiment analysis determines the author's attitude and determines whether it is neutral, positive, or negative. Analysis of these emotions without consideration for voice and facial expressions is crucial and necessitates a supervisory technique for accurate emotion interpretation. Despite these difficulties, it is important to recognize human emotions as they increasingly communicate via social media platforms like Facebook, Twitter, etc. This article examines the difficulties researchers have had studying sentiment analysis in social media as well as other potential issues, and it introduces methods that have been tested through a series of experiments using real datasets and show significant advantages over other approaches currently in use for classifying text in social media.