Affective Computing as a Service (ACaaS)

Abstract

Affective Computing aims to introduce a higher level of computational intelligence to systems, which enables emulation of human affects and emotions. Today those enhanced computing capabilities are seldom found in IT solutions. This paper reviews both Affective Computing and Cloud Computing, presenting the combined outcome in the form of a Software-as-a-Service solution hosted via a Public Cloud Infrastructure. A framework is proposed for the Affective Computing as a Service (ACaaS) solution with the unique consideration that it uses previously created Public Cloud processing services. The framework is then transformed into a working implementation comprising a PHP front-end and a Python back-end. The system is capable of processing text, image, and voice input files and extracting emotional information from them. The results are then presented and evaluated, demonstrating that in most use cases, the multi-modal inputs will facilitate an Affective Computing as a Service solution which will deliver the necessary information for Affective Computing goals. Exploration of the combination of available cloud computing technologies and Affective Computing goals supports research in the area by removing the need for researchers to build their own models. This solution leverages the best available cutting-edge technologies available from large providers. Thereby, the requirement to train new models and the associated overheads are greatly reduced.