# A New Model for Emotion-Driven Behavior Extraction from Text
## 7 Conclusions  
Emotions and their impact on behavior have been extensively studied in many fields. However, the relationships between emotions and behaviors in textual data have received relatively little attention in the field of NLP. To address this gap, this paper introduces the EDBE task along with a novel dataset and a pipeline approach. Our study aims to deepen the understanding of how emotions influence behaviors in text. Our proposed approach exhibits strong performance in extracting emotion-driven behaviors at both the clause and span levels. The evaluation results, including precision, recall, and \(F_{1}\) score metrics, validate the effectiveness of our model in identifying behaviors influenced by emotions. However, our work also has certain limitations. Firstly, in terms of task definition, we focused solely on emotions and their impacts without considering an analysis of the causes of emotions. This limitation indicates a potential avenue for future research to explore the causal factors behind emotions. Secondly, the construction of the dataset was based on a relatively small sample size, which may introduce limitations in terms of generalizability. It is also worth noting that the dataset construction process involved human annotation, and there may be inherent errors resulting from annotation mistakes. Lastly, the pipeline method we proposed for the task may encounter challenges related to error accumulation among different sub-tasks. These limitations provide opportunities for further refinement and improvement in future studies. For future research, several promising directions warrant exploration. Firstly, incorporating advanced techniques, such as leveraging large-scale language models, holds potential for further improving our model's performance. Additionally, expanding the dataset used for EDBE and incorporating ECA can provide a more comprehensive understanding of the impact of emotions on behaviors. Furthermore, investigating the integration of contextual information and exploring the transferability of our model across different domains would be valuable avenues for future exploration. By considering broader contexts and adapting the model to diverse domains, we can enhance the robustness and applicability of our approach. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Clause Level**} & \multicolumn{3}{c}{**Span Level**} \\ \hline
**Template** & \(P\)**(\%)** & \(R\)**(\%)** & \(F_{1}\)**(\%)** & \(P\)**(\%)** & \(R\)**(\%)** & \(F_{1}\)**(\%)** \\ \hline
**Ours** & 83.26 & **89.05** & **86.05** & 72.09 & **77.11** & **74.52** \\ Without \(D\) & 61.91 & 78.86 & 69.36 & 53.91 & 68.66 & 60.39 \\ Without \(\tau\) & 81.06 & 87.31 & 84.07 & 69.98 & 75.37 & 72.57 \\ Without \(s_{e}\) & 49.44 & 88.81 & 63.52 & 42.24 & 75.87 & 54.27 \\ With \(c_{b}\) & **84.26** & 82.59 & 83.42 & **73.10** & 71.64 & 72.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the ablation study on our template. In summary, this study advances the field of emotion analysis in text mining by introducing the EDBE task along with a comprehensive methodology. Through the successful extraction of emotion-driven behaviors, we have enhanced our understanding of the intricate interplay between emotions and behaviors. The proposed pipeline approach not only lays the groundwork for further research but also holds promising potential for real-world applications in areas such as sentiment analysis, mental health, and social sciences. Conceptualization, Y.S. ; methodology, Y.S., S.H. and X.H. ; software, Y.S. ; validation, Y.S. ; formal analysis, Y.S. and R.Z. ; investigation, Y.S. and X.H. ; resources, Y.S. and X.H. ; data curation, Y.S. ; writing--original draft preparation, Y.S. ; writing--review and editing, Y.S., S.H., R.Z. and X.H. ; visualization, Y.S. ; supervision, Y.S. ; project administration, Y.S. ; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.