Argument-Based Sentiment Analysis on Forward-Looking Statements (2024)

Abstract: This paper introduces a novel approach to analyzing the forward-looking statements in equity research reports by integrating argument mining with sentiment analysis. Recognizing the limitations of traditional models in capturing the nuances of future-oriented analysis, we propose a refined categorization of argument units into claims, premises, and scenarios, coupled with a unique sentiment analysis framework. Furthermore, we incorporate a temporal dimension to categorize the anticipated impact duration of market events. To facilitate this study, we present the Equity Argument Mining and Sentiment Analysis (Equity-AMSA) dataset. Our research investigates the extent to which detailed domain-specific annotations can be provided, the necessity of fine-grained human annotations in the era of large language models, and whether our proposed framework can improve performance in downstream tasks over traditional methods. Experimental results reveal the significance of manual annotations, especially for scenario identification and sentiment analysis. The study concludes that our annotation scheme and dataset contribute to a deeper understanding of forward-looking statements in equity research reports.

## introduction the study of argument mining has been a focal point of comprehensive research @xcite @xcite , playing a crucial role in unraveling the complex views of authors or speakers. the foundational toulmin argument model @xcite classifies argument units into claims and premises (evidence) and dissects their interrelations. nevertheless, such a categorization seems overly broad for interpreting the intricacies of forward-looking arguments, those primarily concerned with future analysis @xcite . for example, when predicting future price trends, the toulmin model treats past events and anticipated fu- ## conclusion this paper proposes a novel and comprehensive approach to the automated interpretation of equity research reports by integrating argument mining with sentiment analysis, and introduces the equity-amsa dataset. our results show the indispensable role of human annotation in maintaining highquality data for training machine learning models. despite the advancements in llms, our findings reveal that manual annotations outperform llmgenerated labels in most tasks, highlighting the nuanced understanding humans bring to the interpretation of financial texts. moreover, the predictability assessment through experimental evaluations demonstrates the superior performance of our proposed annotation schemes and the importance of task-specific features in enhancing the analysis of equity research reports. our work serves as a foundation for future exploration, encouraging further investigation into the integration of argument mining and sentiment analysis for the nuanced interpretation.