Welcome to the InfoSci Platform
Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index

Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index

Yoosin Kim, Michelle Jeong, Seung Ryul Jeong
Copyright: © 2015 |Pages: 13
ISBN13: 9781466672727|ISBN10: 1466672722|EISBN13: 9781466672734
DOI: 10.4018/978-1-4666-7272-7.ch003
Cite Chapter Cite Chapter

MLA

Kim, Yoosin, et al. "Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index." Handbook of Research on Organizational Transformations through Big Data Analytics, edited by Madjid Tavana and Kartikeya Puranam, IGI Global, 2015, pp. 30-42. https://doi.org/10.4018/978-1-4666-7272-7.ch003

APA

Kim, Y., Jeong, M., & Jeong, S. R. (2015). Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index. In M. Tavana & K. Puranam (Eds.), Handbook of Research on Organizational Transformations through Big Data Analytics (pp. 30-42). IGI Global. https://doi.org/10.4018/978-1-4666-7272-7.ch003

Chicago

Kim, Yoosin, Michelle Jeong, and Seung Ryul Jeong. "Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index." In Handbook of Research on Organizational Transformations through Big Data Analytics, edited by Madjid Tavana and Kartikeya Puranam, 30-42. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7272-7.ch003

Export Reference

Mendeley
Favorite

Abstract

In light of recent research that has begun to examine the link between textual “big data” and social phenomena such as stock price increases, this chapter takes a novel approach to treating news as big data by proposing the intelligent investment decision-making support model based on opinion mining. In an initial prototype experiment, the researchers first built a stock domain-specific sentiment dictionary via natural language processing of online news articles and calculated sentiment scores for the opinions extracted from those stories. In a separate main experiment, the researchers gathered 78,216 online news articles from two different media sources to not only make predictions of actual stock price increases but also to compare the predictive accuracy of articles from different media sources. The study found that opinions that are extracted from the news and treated with proper sentiment analysis can be effective in predicting changes in the stock market.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.