skip to main content
10.1145/3184558.3191825acmotherconferencesArticle/Chapter ViewAbstractPublication PageswwwConference Proceedingsconference-collections
research-article
Free Access

Aspect-based Financial Sentiment Analysis with Deep Neural Networks

Published:23 April 2018Publication History

ABSTRACT

Aspect-based financial sentiment analysis, which aims to classify the text instance into a pre-defined aspect class and predict the sentiment score for the mentioned target. In this paper, we propose a neural network model, Attention-based LSTM model with the Aspect information (ALA), to solve the financial opinion mining problem introduced by the WWW 2018 shared task. The proposed neural network model can adapt to the financial dataset so that the neural network can effectively understand the semantic information of the short text. We evaluate our model with the 10-fold cross-validation, and we compare our model with a variety of related deep neural network models.

References

  1. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research Vol. 12, Aug (2011), 2493--2537. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Jacob Devlin, Rabih Zbib, Zhongqiang Huang, Thomas Lamar, Richard M Schwartz, and John Makhoul. 2014. Fast and Robust Neural Network Joint Models for Statistical Machine Translation. ACL (1). 1370--1380.Google ScholarGoogle Scholar
  3. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation Vol. 9, 8 (1997), 1735--1780. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014. A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014).Google ScholarGoogle Scholar
  5. Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014).Google ScholarGoogle Scholar
  6. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google ScholarGoogle Scholar
  7. Ji Young Lee and Franck Dernoncourt. 2016. Sequential short-text classification with recurrent and convolutional neural networks. arXiv preprint arXiv:1603.03827 (2016).Google ScholarGoogle Scholar
  8. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).Google ScholarGoogle Scholar
  9. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 b. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Radim v Rehr uv rek and Petr Sojka. 2010. Software Framework for Topic Modelling with Large Corpora Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. ELRA, Valletta, Malta, 45--50. http://is.muni.cz/publication/884893/enGoogle ScholarGoogle Scholar
  11. Richard Socher, John Bauer, Christopher D Manning, and Andrew Y Ng. 2013. Parsing with Compositional Vector Grammars. In ACL (1). 455--465.Google ScholarGoogle Scholar
  12. Duyu Tang, Bing Qin, and Ting Liu. 2016. Aspect Level Sentiment Classification with Deep Memory Network Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 214--224.Google ScholarGoogle Scholar

Index Terms

  1. Aspect-based Financial Sentiment Analysis with Deep Neural Networks

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          WWW '18: Companion Proceedings of the The Web Conference 2018
          April 2018
          2023 pages
          ISBN:9781450356404

          Copyright © 2018 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          International World Wide Web Conferences Steering Committee

          Republic and Canton of Geneva, Switzerland

          Publication History

          • Published: 23 April 2018

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate1,899of8,196submissions,23%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format