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Application of Deep Neural Network M...

Zhang, Liang.

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Application of Deep Neural Network Models to Financial Opinion Mining.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Application of Deep Neural Network Models to Financial Opinion Mining./
Author:	Zhang, Liang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	83 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
Contained By:	Dissertations Abstracts International80-05B.
Subject:	Finance. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10930862
ISBN:	9780438585850

Application of Deep Neural Network Models to Financial Opinion Mining.
Zhang, Liang.

Application of Deep Neural Network Models to Financial Opinion Mining. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 83 p.

Source: Dissertations Abstracts International, Volume: 80-05, Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2018.

This item must not be sold to any third party vendors.

Domain-specific sentiment analysis has obtained an increasing attention from the Natural Language Processing community. However, we find a lack of studies seeking to identify actual opinions in financial articles and comments with bullish or bearish investment recommendations. In our work, we focus on applying neural network models to sentiment analysis task in financial domain. It mainly consists of three parts: (1) we develop an effective lexicon to accurately differ positive and negative sentiments of investors that can help direct financial comments to their actual meanings. Specifically, we firstly apply an efficient prediction-based neural network model to generate initial domain-specific lexicons for short financial texts, such as bullish and bearish comments in tweets, blogs and news headlines. Then, an embedding subspace projection method with 1-vs-rest final layer of sigmoids is utilized to further reduce the unwanted neutral expressions in the induced lexicons; (2) we propose a context-aware deep embedding network for investment recommendation tweets sentiment analysis, by jointly encoding the global and local contextual information. Especially, we capture and include an attitude-aware user embedding to enhance the performance of our model; (3) we identify long and short investment opinions from well-structured summary text of investment recommendation blogs. Current methods tend to regard it as a general text categorization task without considering the inherent content and linguistic specialties. By contrast, we propose to design an architecture that fully utilizes aspects and temporal references knowledge. More specifically, we extend a hierarchical attention neural network by adding important words supervision, which enables the model to pay more attention to sharp words and nearby contexts. In particular, aspects and temporal references position-aware attention mechanism is utilized to achieve focal attention. Experiments on financial lexicons, tweets, and article summary, show that our proposed architectures outperform the state-of-the-art.

ISBN: 9780438585850Subjects--Topical Terms:

542899
Finance.

Application of Deep Neural Network Models to Financial Opinion Mining.
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300 $a 83 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Li, Xiaolin;Wu, Song.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a Domain-specific sentiment analysis has obtained an increasing attention from the Natural Language Processing community. However, we find a lack of studies seeking to identify actual opinions in financial articles and comments with bullish or bearish investment recommendations. In our work, we focus on applying neural network models to sentiment analysis task in financial domain. It mainly consists of three parts: (1) we develop an effective lexicon to accurately differ positive and negative sentiments of investors that can help direct financial comments to their actual meanings. Specifically, we firstly apply an efficient prediction-based neural network model to generate initial domain-specific lexicons for short financial texts, such as bullish and bearish comments in tweets, blogs and news headlines. Then, an embedding subspace projection method with 1-vs-rest final layer of sigmoids is utilized to further reduce the unwanted neutral expressions in the induced lexicons; (2) we propose a context-aware deep embedding network for investment recommendation tweets sentiment analysis, by jointly encoding the global and local contextual information. Especially, we capture and include an attitude-aware user embedding to enhance the performance of our model; (3) we identify long and short investment opinions from well-structured summary text of investment recommendation blogs. Current methods tend to regard it as a general text categorization task without considering the inherent content and linguistic specialties. By contrast, we propose to design an architecture that fully utilizes aspects and temporal references knowledge. More specifically, we extend a hierarchical attention neural network by adding important words supervision, which enables the model to pay more attention to sharp words and nearby contexts. In particular, aspects and temporal references position-aware attention mechanism is utilized to achieve focal attention. Experiments on financial lexicons, tweets, and article summary, show that our proposed architectures outperform the state-of-the-art.
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