Natural Language Processing to Assess Communication Dynamics between Cooperating Dyads during Video Gameplay

Abstract

Latent Dirichlet Allocation (LDA) and Sentiment Analysis have become prominent tools in natural language processing applications for both research and industry. While LDA is a generative probabilistic modeling methodology that is widely used in Topic Modeling to extract underlying themes and topics from a collection of words, Sentiment Analysis is defined as identifying the hedonic tone of a corpus of text. Here, supervised Sentiment Analysis is used to classify conversations between team gaming dyads in terms of valence. Additionally, LDA is utilized to label segments of cooperative conversation between dyads as topics. Fourteen participants were paired as dyads (7 teams) and instructed to complete thirty-two 150 second gaming scenarios (trials) in the first-person shooter (FPS) video game Overwatch. While completing the scenarios, participants were instructed to communicate with their respective teammate via a voice communication headset. The conversations from each scenario were transcribed from recorded voice channels before analysis was performed. Our approach examines the relationship between perceived task difficulty and both conversation sentiment scores and topic frequency in both novice experienced skill groups. Preliminary results indicate evidence that conversation topic, sentiment and perception dynamics are consistent with an encouragement and frustration sentiment paradigm.

Keywords: communication, natural language processing, topic modeling

DOI: 10.54941/ahfe1001827