Rating City Atmosphere Using Onomatopoeia and Prediction from Statistical Information

Abstract

This paper presents a method for building a regression model that predicts the atmosphere of cities represented in onomatopoeia scales by using fundamental statistics of cities, such as population and number of restaurants. To build our regression model, we conducted two experiments, one is conducted to select suitable onomatopoeia for scales. In this onomatopoeia selection experiment, we asked participants to answer the onomatopoeia that represents the atmosphere around the station. In the other annotation experiment, we conducted questionnaire to quantify the atmosphere in onomatopoeia scales. Our regression model was a support vector regression model built from training data collected in the annotation experiment and statistical information. As a result, accuracy of our regression model for several target atmosphere ("kibi-kibi", "howa-howa", "yuru-yuru" and "iso-iso") was greater than 0.5.