A Method of Sentiment Polarity Identification in Financial News using Deep Learning | Zendy

Daisuke Katayama | Zendy; Yasunobu Kino | Zendy; Kazuhiko Tsuda | Zendy

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A Method of Sentiment Polarity Identification in Financial News using Deep Learning

Author(s) -

Daisuke Katayama,

Yasunobu Kino,

Kazuhiko Tsuda

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.09.298

Subject(s) - computer science , polarity (international relations) , sentiment analysis , identification (biology) , artificial intelligence , deep learning , stock (firearms) , natural language processing , machine learning , mechanical engineering , genetics , botany , cell , engineering , biology

In this research, sentiment polarity identification model for finance is developed using financial and economic corpus and deep learning. Specifically, “Japanese Economy Watchers Survey” is used for the corpus and our model accuracy is high. Then the model is applied to evaluate news sentiment for predicting stock return. Our results confirmed that our model captures more news sentiment compared to using common polarity dictionary.

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