# A New Model for Emotion-Driven Behavior Extraction from Text
## 1 Introduction  
Emotion analysis of text has emerged as a pivotal topic in the field of natural language processing (NLP) [1]. While initial research efforts have focused mainly on identifying expressions of emotion, such as emotion classification [2, 3, 4] and emotion information extraction [5], there has been a growing interest among researchers in understanding the underlying causes of emotions. Consequently, the field of emotion cause analysis (ECA) has gained prominence as a burgeoning area of research. An ECA primarily aims to extract the causes that trigger emotional expression in text. However, most of the existing studies in this domain have focused on investigating the causes of individual emotions while overlooking the influence of these emotions on corresponding behaviors. It is crucial to note that emotions significantly impact people's actions and behaviors [6], and these emotion-driven behaviors can have broader effects on others and on society as a whole. Therefore, it is imperative to extend the research focus beyond the causes of emotions alone and explore the intricate relationships between emotions and behaviors. The impact of emotions on various aspects of human behavior has been extensively studied across different fields. Within the realms of cognitive psychology and neuroscience,research has highlighted the distinct effects of emotions on behavior [7; 8; 9; 10], attention [11], memory [12; 13], and growth [14]. Insights from sociology and law have linked emotions to negative behavior, such as risky driving [15], destructive behavior in children [16], and criminal activities [17]. Additionally, research in the field of sociology has demonstrated the impact of emotion regulation on individual strain [18] and leadership effectiveness [19]. Moreover, in the realm of computer science, the integration of emotional characteristics has shown promise in enhancing the performance of recommendation systems [20; 21]. Collectively, these findings underscore the relevance and significance of research in this field. The above studies have shown that research on emotions and their impacts is highly respected in academia. However, in the field of NLP, corresponding research is still lacking. The majority of studies for emotion analysis in the field of NLP have primarily focused on investigating emotions and their causes rather than exploring the consequences and outcomes associated with emotions. Research within this specific domain has predominantly emphasized understanding the underlying reasons behind emotions, leaving a noticeable gap in comprehending the effects and implications of emotions. In this paper, we present a novel task called emotion-driven behavior extraction (EDBE), which aims to automatically extract behavior driven by emotions from text. Here, \"behavior\" refers to the description of a certain action or conduct within the text. The ultimate goal of this research is to efficiently extract the expressed emotions and their resulting impact information from massive amounts of text, which holds potential value for subsequent sociological analysis. To address the EDBE task, we create a publicly available annotated dataset. Furthermore, we propose a two-step pipeline approach. The first step involves extracting emotions and behaviors from the text, which is a well-established task in NLP. The second step involves matching emotions with the behaviors they drive, posing a more significant challenge due to the complex nature of the relationships between emotions and behaviors. The main novelties of this work can be summarized as follows. * Introducing a novel task: We introduce a novel task called emotion-driven behavior extraction (EDBE), which shifts the focus from ECA to understanding the impact of emotions on behaviors. This task holds significant practical value as it provides insights into the relationships between emotions and behaviors. Furthermore, this work makes a significant theoretical contribution to the field of emotion analysis of text.