Exploring Geographical Crowd’s Emotions with Twitter

Abstract

The research introduced in this paper develops a semantic model whose objective is to analyze the geographical and emotion-based distribution of tweets at a large country scale. The approach extracts and categorizes tweets based on semantic orientations of terms in a dictionary, and explores their spatial and temporal distribution. Tweets are classified into different emotional classes, qualified and valued using different interval distributions that favor identification of significant trends that are compared to some of the main properties of the underlying geographical space. The whole approach is applied to a large tweets database in Japan, and illustrated by some experimental but real data that trigger some surprising and puzzling outcomes that are discussed in the paper.