Human aggressiveness and reactions towards uncertain decisions

Abstract

Big data is a term that defines data sets are so large or complex that traditional data processing applications are inadequate. Data comes with different formats such as Multilanguage, structured, unstructured and emails. Challenges dealing with a huge amount of data includes analysis, search, sharing, transfer, and visualization. Not all the data collected contains useful information, so there's a need to refine this data in order to filter out the useful information. Tweets posted on Twitter are expressed as opinions. These opinions can be used for different purposes such as to take public views on uncertain decisions. These decisions have a direct impact on the user’s life such as violations and aggressiveness are common causes. For this purpose, we have collected opinions on some popular decision taken in the past decade from Twitter. We have divided the tweet text into two classes; Anger (negative) and positive. We have proposed a prediction model to predict public opinions towards such decisions. We used Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR) classifier for a text classification task. Furthermore, we have also compared SVM results with NB, LR. The research will help us to predict early behaviors and reactions of people before the big consequences of such decisions. Moreover, the results highlight the feasibility of using social media to predict public opinions.