Dynamic Analysis of Demographic Sentiment

Abstract

There is no doubt that big data analysis has a very positive impact on economics, security, and other aspects for countries and enterprises alike. Where we have recently noticed the frantic competition between companies to increase their profits by analyzing the largest amount of data as quickly as possible. Especially analyzing data related to Covid-19 to make the most of information in all areas. Covid-19 has drastically affected many lives in recent years but, even in these hard times, businesses can leverage the current pandemic to make a profit. In this paper, we investigate a variety of tweets using MapReduce, Spark, and Machine Learning methods to determine the sentiment of a given tweet based on the information provided by the dataset. With this information, businesses could learn how to present Covid-19 and pandemic related goods and information in a way that will be well received by its audience. To take this a step further, we will investigate trends in sentiment across demographics tweeting about the virus. This information in sentiment is dynamically useful to understand how specific audiences feel about the pandemic. We explore which Machine Learning methods produce the best results such as Multi-Layer Perceptron neural networks and Logistic Regression.