Carrot and Stick: Inducing Self-Motivation with Positive & Negative Feedback

TL;DR

This paper introduces the CASTIC dataset, comprising sentences with positive and negative feedback strategies, to study and enhance self-motivation from a computational perspective, addressing a gap in existing research.

Contribution

The paper presents the first dataset focusing on both positive and negative feedback strategies for self-motivation, enabling computational analysis of motivational language.

Findings

Created a dataset of 12,590 sentences with motivational strategies

Facilitates research on computational modeling of self-motivation

Provides publicly available data and code for further study

Abstract

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.