Deep Learning for Financial Sentiment Analysis on Finance News Providers

doi:10.1109/ASONAM.2016.7752381

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/108798

Title:	Deep Learning for Financial Sentiment Analysis on Finance News Providers
Authors:	Min-Yuh Day;Chia-Chou Lee
Keywords:	Deep Learning;Financial Sentiment Analysis;Financial Technology (FinTech);Finance News Providers;Stock Prediction
Date:	2016-08-18
Issue Date:	2016-12-13 02:10:44 (UTC+8)
Publisher:	ACM
Abstract:	Investors have always been interested in stock price forecasting. Since the development of electronic media, hundreds pieces of financial news are released on different media every day. Numerous studies have attempted to examine whether the stock price forecasting through text mining technology and machine learning could lead to abnormal returns. However, few of them involved the discussion on whether using different media could affect forecasting results. Financial sentiment analysis is an important research area of financial technology (FinTech). This research focuses on investigating the influence of using different financial resources to investment and how to improve the accuracy of forecasting through deep learning. The experimental result shows various financial resources have significantly different effects to investors and their investments, while the accuracy of news categorization could be improved through deep learning.
Relation:	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016)
DOI:	10.1109/ASONAM.2016.7752381
Appears in Collections:	[Graduate Institute & Department of Information Management] Proceeding

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