An Effective Sentiment Analysis of Social Media Data Using Deep Recurrent Neural Network Models

Abstract

Today we can do a lot of analytics and statistics using social media data. The world had been exposed to the COVID-19 pandemic recently. With the rapid increase in epidemics and deaths, people have different feelings about the disease. Collecting and reading boring tweets creates real emotions in these difficult times. The purpose of this study is to provide a specific perspective for understanding the emotions people around the world experience in relation to this condition. To do this, various Tweets related to a specific domain are received through the Twitter platform. When Tweets are collected, they are categorized and read to effectively reflect the Tweet's true sentiment for COVID-19.Natural Language Processing(NLP) is used to analyze the review comments from social media and Recurrent Neural Network (RNN) for sentiment classification .The model identifies the emotional polarity of "conversations" often work with ambiguous tweets, reduced accuracy, and reduced tolerances. Also the RNN model defines the expression of emotions occurring in a subject at any given time.