Combining Natural Language and Machine Learning for Predicting Survey Responses of Social Constructs in a Dyad

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Calfa, Bruno Abreu
Sanaei, Mohammadamin
Wu, Peggy
Radlbeck, Andrew
Israelsen, Brett
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Gilbert, Stephen
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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Virtual Reality Applications Center
At VRAC, our mission is clear: “To elevate the synergy between humans and complex interdisciplinary systems to unprecedented levels of performance”. Through our exceptional Human Computer Interaction (HCI) graduate program, we nurture the next generation of visionaries and leaders in the field, providing them with a comprehensive understanding of the intricate relationship between humans and technology. This empowers our students to create intuitive and transformative user experiences that bridge the gap between innovation and practical application.
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Measuring social constructs such as engagement, rapport, and trust often rely heavily on surveys and behavioral observations. This paper describes a method to use features identified by psychology-based language analysis, combined with machine learning, to predict participant survey responses in a training context based on 120 dyad transcripts. The method analyzed data collected from subjects performing a circuit board training task within the project called SCOTTIE, Systematic Communication Objectives and Telecommunications Technology Investigations and Evaluations. In this study, the collected data showed low utterance count and a lack of correlation between features and survey responses, suggesting that the context in which the interactions occurred may limit opportunities for interlocutors to manifest social behaviors verbally, which in turn affected the ability to use language analysis to predict subject perceptions of the interaction. However, the methodology appears sound.
This is a manuscript of a proceeding published as Calfa, Bruno Abreu, Peggy Wu, Mohammadamin Sanaei, Stephen Gilbert, Andrew Radlbeck, and Brett Israelsen. "Combining Natural Language and Machine Learning for Predicting Survey Responses of Social Constructs in a Dyad." In 2022 IEEE 2nd International Conference on Intelligent Reality (ICIR), pp. 58-61. IEEE, 2022. DOI: 10.1109/ICIR55739.2022.00028. Copyright 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Posted with permission.