Sentiment analysis on 2D images of urban and indoor spaces using deep learning architectures

Abstract

This paper focuses on the determination of the evoked sentiments to people by observing outdoor and indoor spaces, aiming to create a tool for designers and architects that can be utilized for sophisticated designs. Since sentiment is subjective, the design process can be facilitated by an ancillary automated tool for sentiment extraction. Simultaneously, a dataset containing both real and virtual images of vacant architectural spaces is introduced, while the SUN attributes are also extracted from the images in order to be included throughout training. The dataset is annotated towards both valence and arousal, while five established and two custom architectures, one which has never been used before in classifying abstract concepts, are evaluated on the collected data.