Carrot and Stick: Inducing Self-Motivation with Positive & Negative Feedback
This work addresses the problem of inducing self-motivation for applications in education and the workplace, but it is incremental as it primarily provides a new dataset without major methodological breakthroughs.
The paper tackles the lack of computational study on self-motivation by introducing the CASTIC dataset with 12,590 sentences using five strategies, including both positive and negative feedback, to enhance self-motivation.
Positive thinking is thought to be an important component of self-motivation in various practical fields such as education and the workplace. Previous work, including sentiment transfer and positive reframing, has focused on the positive side of language. However, self-motivation that drives people to reach their goals has not yet been studied from a computational perspective. Moreover, negative feedback has not yet been explored, even though positive and negative feedback are both necessary to grow self-motivation. To facilitate self-motivation, we propose CArrot and STICk (CASTIC) dataset, consisting of 12,590 sentences with 5 different strategies for enhancing self-motivation. Our data and code are publicly available at here.