Collecting users’ engagement is crucial for gathering feedback about product features and can allow requirement analysts to have a better understanding of the user and the product. Current approaches being used to collect and analyze user’s feedback are based on techniques such as product reviews and social media. The problem with these techniques is that they are hard to apply to specific needs. Due to this companies used interviews to get feedback on their products. In this research, we proposed the use of biofeedback, voice, and supervised machine learning algorithms to allow users to have additional information about the interviewees’ engagement during the interview. We conducted an experiment that consisted of interviewing users while gathering their biofeedback using an Empatica E4 wristband and capturing their voice using the default audio recorded of a common-laptop.
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Mon 12 Apr
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