Automated tool for conducting emotion analysis studies in perception surveys

Abstract

Considering the growing need for companies to automate the analysis of customer opinions from different digital media, this paper outlines the development of an automated tool for emotion analysis in survey responses utilizing Ekman’s six-emotion model (joy, excitement, anger, sadness, fear, and boredom). The tool processes spreadsheets containing qualitative responses and generates the percentage distribution of emotions at both individual and aggregated levels. A case study conducted with 46 systems engineering students at the University of Cartagena during the COVID-19 pandemic showed that 'anger' was the most prevalent emotion (29.3%), followed by 'excitement' (19.4%), while 'boredom' was the least frequent (2.6%). The tool demonstrated an accuracy rate of 92% in classifying emotions, compared to 90% achieved through manual coding. These results highlight the tool’s effectiveness in automating emotion analysis, providing statistical and graphical reports that aid decision-making in academic and organizational contexts.