{"type":"ScholarlyArticle","title":"Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention","authors":[{"name":"Ling Luo","slug":"ling-luo","given":"Ling","family":"Luo","affiliation":"Key Lab of Intelligent Information Processing , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences"},{"name":"Xiang Ao","slug":"xiang-ao","given":"Xiang","family":"Ao","affiliation":"Key Lab of Intelligent Information Processing , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences"},{"name":"Feiyang Pan","slug":"feiyang-pan","given":"Feiyang","family":"Pan","affiliation":"Key Lab of Intelligent Information Processing , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences"},{"name":"Jin Wang","slug":"jin-wang","given":"Jin","family":"Wang","affiliation":"Computer Science Department , UCLA"},{"name":"Tong Zhao","slug":"tong-zhao","given":"Tong","family":"Zhao","affiliation":"Deloitte China"},{"name":"Ningzi Yu","slug":"ningzi-yu","given":"Ningzi","family":"Yu","affiliation":"Deloitte China"},{"name":"Qing He","slug":"qing-he","given":"Qing","family":"He","affiliation":"Key Lab of Intelligent Information Processing , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences"}],"abstract":"Sentiment analysis has played a significant role in financial applications in recent years. The informational and emotive aspects of news texts may affect the prices, volatilities, volume of trades, and even potential risks of financial subjects. Previous studies in this field mainly focused on identifying polarity~(e.g. positive or negative). However, as financial decisions broadly require justifications, only plausible polarity cannot provide enough evidence during the decision making processes of humanity. Hence an explainable solution is in urgent demand. In this paper, we present an interpretable neural net framework for financial sentiment analysis. First, we design a hierarchical model to learn the representation of a document from multiple granularities. In addition, we propose a query-driven attention mechanism to satisfy the unique characteristics of financial documents. With the domain specified questions provided by the financial analysts, we can discover different spotlights for queries from different aspects. We conduct extensive experiments on a real-world dataset. The results demonstrate that our framework can learn better representation of the document and unearth meaningful clues on replying different users? preferences. It also outperforms the state-of-the-art methods on sentiment prediction of financial documents.","doi":"10.24963/ijcai.2018/590","url":"https://ijcai.org/proceedings/2018/590","pdf":"https://ijcai.org/proceedings/2018/0590.pdf","markdown":"https://ijcai.org/proceedings/2018/590.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/590","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":590,"pages":{"first":4244,"last":4250},"keywords":["Machine Learning: Data Mining","Natural Language Processing: Sentiment Analysis and Text Mining","Multidisciplinary Topics and Applications: Finance"],"videos":[],"references":[{"position":1,"raw":"[Akhtar et al., 2017] Md Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, and Pushpak Bhat- tacharyya. 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