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    Implicit and Explicit Aspect Extraction in Financial Microblogs

    Gaillat, T. and Stearns, B. and McDermott, R. and Sridhar, G. and Zarrouk, Manel and Davis, Brian (2018) Implicit and Explicit Aspect Extraction in Financial Microblogs. In: ECONLP 2018 – 1st Workshop on Economics and Natural Language Processing, 20 July 2018, Melbourne, Australia.

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    This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.

    Item Type: Conference or Workshop Item (Paper)
    Additional Information: This work is funded by the SSIX Horizon 2020 project (Grant agreement No 645425)
    Keywords: Implicit; Explicit; Aspect; Extraction; Financial Microblogs;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 13417
    Depositing User: Brian Davis
    Date Deposited: 07 Oct 2020 15:00
    Refereed: Yes
    Funders: European Union Horizon 2020 programme
      Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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