Sentiment Analysis of Data in Social Networking Sites using Machine Learning Approaches

Abstract

The emergence of various social networking platforms has made it incredibly easy for individuals to create, express, and share their ideas, thoughts, opinions, and emotions with a global audience. With the rapid advancement of technology, miniature computers and Smartphone have become ubiquitous, allowing people to easily convey their thoughts on social media platforms like Facebook, Twitter, Wikipedia, LinkedIn, Google+, Instagram, and more.
Thanks to the exponential growth in both population and communication technologies over the past decade, the use of social networks has surged, and they are now employed for a multitude of purposes. One promising application that has garnered attention is the analysis of users' posts to detect signs of depression.
In this paper, we explore how it is possible to gauge the level of depression in an individual by observing and extracting emotional cues from their text-based content. We achieve this through the utilization of emotion theories, machine learning techniques, and natural language processing tools across various social media platforms.