Weibo Mood Towards Stock Market

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications
Subtitle of host publicationDASFAA 2016 International Workshops: BDMS, BDQM, MoI, and SeCoP, Dallas, TX, USA, April 16-19, 2016, Proceedings
EditorsHong Gao, Jinho Kim, Yasushi Sakurai
PublisherSpringer
Pages3-14
ISBN (Electronic)978-3-319-32055-7
ISBN (Print)978-3-319-32054-0
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
VolumeLNCS 9645
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

TitleThe 21st International Conference on Database Systems for Advanced Applications, DASFAA 2016
LocationThe University of Texas at Dallas
PlaceUnited States
CityDallas
Period16 - 19 April 2016

Abstract

Behavioral economics and behavioral finance believe that public mood is correlated with economic indicators and financial decisions are signifi‐ cantly driven by emotions. A growing body of research has examined the corre‐ lation between stock market and social media public mood state. However most research is conducted on English social media websites, the number of research on how public mood states in Chinese social media websites affect the stock market in China is limited. This paper first summarizes the previous research on text mining and social media sentiment analysis. After that, we investigate whether measurements of collective public mood states derived from Weibo which is a social media website similar as Twitter but most posts are written in Chinese are correlated to the stock market price in China. We use a novel Chinese mood extracting method using two NLP (Natural Language Processing) tools: Jieba and Chinese Emotion Words Ontology to analyze the text content of daily Weibo posts. A Granger Causality analysis is then used to investigate the hypoth‐ esis that the extracted public mood or emotion states are predictive of the stock price movement in China. Our experimental results indicate that some public mood dimensions such as “Happiness” and “Disgust” are highly correlated with the change of stock price and we can use them to forecast the price movement.

Research Area(s)

  • Sentiment analysis, Text mining, Behavioral finance, Twitter, Weibo chinese emotion words ontology

Citation Format(s)

Weibo Mood Towards Stock Market. / Chen, Wen Hao; Cai, Yi; Lai, Kin Keung.

Database Systems for Advanced Applications: DASFAA 2016 International Workshops: BDMS, BDQM, MoI, and SeCoP, Dallas, TX, USA, April 16-19, 2016, Proceedings. ed. / Hong Gao; Jinho Kim; Yasushi Sakurai. Springer, 2016. p. 3-14 (Lecture Notes in Computer Science; Vol. LNCS 9645).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)