Motives and emotions are essential components of the inner experience, and emotions provide feedback about the progress and significance of an individual’s motivated actions. However, since the continuous stream of emotional and motivational experiences makes it difficult to access these concepts consciously, this in turn makes them difficult to assess using self-report methods such as questionnaires, requiring “at a distance” measures such as implicit motive coding (Schultheiss & Pang 2007) which are time-consuming and impractical for continuously streaming data. The advent of machine learning reveals a potential approach for assessing motivation and emotions at the same time, and identifying their patterns of co-occurrence in different motivationally relevant contexts. In this presentation, we report on the development of a machine learning tool for assessing implicit power, achievement, and affiliation motivation in various natural language contexts (e.g., emails, stories, social media data, newsletters, etc.), and compare its performance with hand-coded data that tracks implicit motives in 3,944 emotion-tagged Tweets from four World Cup matches, identifying significant correspondences between implicit motives and emotion (need for power negatively correlated with eagerness/joy; need for achievement negatively correlated with fear/sadness and positively correlated with eagerness). Such findings highlight the differential pattern of emotional reactions relative to personality, which has broader implications for understanding congruence of personality and emotional expression in other linguistic contexts. We discuss our findings under the larger question of whether natural language processing provides an effective means of investigating the co-occurrence of motive and emotion imagery at scale.

Authors:
Joyce S. Pang, Nanyang Technological University, Singapore
Hiram Ring, Nanyang Technological University, Singapore
Aretha Wan, Nanyang Technological University, Singapore
Alexa Khoo, Nanyang Technological University, Singapore

About the Presenter(s)
Professor Joyce S. Pang is a University Associate Professor/Senior Lecturer at Nanyang Technological University in Singapore

See this presentation on the full schedule – Thursday Schedule

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