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    <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.</abstract>
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%0 Conference Proceedings
%T Argument-Based Sentiment Analysis on Forward-Looking Statements
%A Lin, Chin-Yi
%A Chen, Chung-Chi
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F lin-etal-2024-argument
%X 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.
%U https://aclanthology.org/2024.findings-acl.820
%P 13804-13815

Markdown (Informal)

[Argument-Based Sentiment Analysis on Forward-Looking Statements](https://aclanthology.org/2024.findings-acl.820) (Lin et al., Findings 2024)

• Argument-Based Sentiment Analysis on Forward-Looking Statements (Lin et al., Findings 2024)

ACL

• Chin-Yi Lin, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2024. Argument-Based Sentiment Analysis on Forward-Looking Statements. In Findings of the Association for Computational Linguistics ACL 2024, pages 13804–13815, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.