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        <title>Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets</title>
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    <abstract>The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors. Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving. This study, instead of proposing any “new model”, investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors. These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.</abstract>
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%0 Conference Proceedings
%T Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets
%A Xing, Frank
%A Malandri, Lorenzo
%A Zhang, Yue
%A Cambria, Erik
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F xing-etal-2020-financial
%X The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors. Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving. This study, instead of proposing any “new model”, investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors. These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.
%R 10.18653/v1/2020.coling-main.85
%U https://aclanthology.org/2020.coling-main.85
%U https://doi.org/10.18653/v1/2020.coling-main.85
%P 978-987

Markdown (Informal)

[Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets](https://aclanthology.org/2020.coling-main.85) (Xing et al., COLING 2020)

• Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets (Xing et al., COLING 2020)

ACL

• Frank Xing, Lorenzo Malandri, Yue Zhang, and Erik Cambria. 2020. Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets. In Proceedings of the 28th International Conference on Computational Linguistics, pages 978–987, Barcelona, Spain (Online). International Committee on Computational Linguistics.