Modeling public mood and emotion: Blog and news sentiment and socio-economic phenomena

被引：33

作者：

Chen, Mu-Yen ^{[1
]}

Chen, Ting-Hsuan ^{[2
]}

机构：

[1] Natl Taichung Univ Sci & Technol, Dept Informat Management, Taichung 404, Taiwan

[2] Natl Taichung Univ Sci & Technol, Dept Finance, 129 Sec 3,San Min Rd, Taichung 404, Taiwan

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2019年 / 96卷

关键词：

Sentiment mining analysis; Text mining; Opinion mining; Stock price; Public mood and emotion; CLASSIFICATION; NETWORK;

D O I：

10.1016/j.future.2017.10.028

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The development of online virtual communities has raised the importance in analyzing massive volume of text from websites and social networks. This research analyzed financial blogs and online news articles to develop a public mood dynamic prediction model for stock markets, referencing the perspectives of behavioral finance and the characteristics of online financial communities. This research applies big data and opinion mining approaches to the investors' sentiment analysis in Taiwan. The proposed model was verified using experimental datasets from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news over an 18 month period. Empirical results indicate the big data analysis techniques to assess emotional content of commentary on current stock or financial issues can effectively forecast stock price movement. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：692 / 699

页数：8

共 39 条

[1]

Abbasi A., 2007, ACM T INFORM SYST, V26, P1

[2] An information-theoretic perspective of tf-idf measures [J].

Aizawa, A .

INFORMATION PROCESSING & MANAGEMENT, 2003, 39 (01) :45-65

[3]

[Anonymous], 2008, AUSDM

[4]

[Anonymous], 2013, RBC CAP MARK ADV AG

[5]

[Anonymous], 2004, Proceedings of the 20th international conference on Computational Linguistics, page, DOI DOI 10.3115/1220355.1220555

[6]

[Anonymous], 2014, SOC MED FIN MARK COM

[7]

[Anonymous], TWITTER FUELED HEDGE

[8]

[Anonymous], 2009, P 14 AUSTRALASIAN DO

[9]

Bollen J., 2011, Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, V11, P450

[10] Twitter mood predicts the stock market [J].

Bollen, Johan ;

Mao, Huina ;

Zeng, Xiaojun .

JOURNAL OF COMPUTATIONAL SCIENCE, 2011, 2 (01) :1-8

← 1 2 3 4 →