A practical application for sentiment analysis on social media textual data

Abstract

With the amount of data that is available today in textual form, it is essential to be able to extract as much useful information as possible from them. While some textual documents are easy to be understood, other textual documents may need extra processes to discover the hidden information within it. For instance, how the author was feeling while writing this piece of text, or what emotions authors are expressing in this piece of text. The idea of discovering what emotions are expressed in a textual document is known as sentiment analysis. The interest in sentiment analysis has been steadily growing in the past decade. Being able to accurately detect and measure the different emotions present in a text has become more and more useful as the availability of online resources has increased. These resources can range from product reviews to social media content. Each of these resources presents their own distinct challenges while still sharing the core techniques and procedures. In this paper, we introduce an application that can detect four distinct emotions from social media posts. We will first outline the techniques we have used as well as our outcomes, then discuss the challenges that we faced, and finally, our proposed solutions for the continuation of this project.