Effective Sentiment Analysis of Corporate Financial Reports

Thirty Fourth International Conference on Information Systems, Milan, 2013

9 Pages Posted: 20 Oct 2013

See all articles by Jimmy Ren

Jimmy Ren

Lenovo Research Hong Kong; City University of Hong Kong (CityUHK) - Department of Information Systems

Huizhong Ge

City University of Hong Kong (CityUHK)

Xiaoyu Wu

City University of Hong Kong (CityUHK)

Guan Wang

City University of Hong Kong (CityUHK)

Wei Wang

City University of Hong Kong (CityUHK)

Stephen Liao

City University of Hong Kong (CityUHK)

Date Written: October 18, 2013

Abstract

Sentiment analysis is widely adopted in studying various important topics in business intelligence. Though many studies reported interesting results by using machine learning, the lack of theoretic analysis and the shortage of practical guidance are hurdles of theory development. Besides, due to the difficulty in labelling data, the effectiveness of sentiment analysis with only labelled data needs to be questioned. In this paper, we drew on statistical learning theory to perform extensive theoretic analysis in sentiment analysis by using real corporate financial reports. We investigated when and why machine learning methods provide preferred performance under the guidance of the theory. We also provided practical suggestions in applying machine learning methods for both researchers and practitioners. In addition, we utilized the cheap and ubiquitous unlabelled data to further improve the sentiment analysis performance. This has the potential to largely reduce the manual data labelling work and to scale up the experiments.

Keywords: Text classification, sentiment analysis, machine learning, unlabeled data

Suggested Citation

Ren, Jimmy and Ge, Huizhong and Wu, Xiaoyu and Wang, Guan and Wang, Wei and Liao, Stephen, Effective Sentiment Analysis of Corporate Financial Reports (October 18, 2013). Thirty Fourth International Conference on Information Systems, Milan, 2013. Available at SSRN: https://ssrn.com/abstract=2342450

Jimmy Ren (Contact Author)

Lenovo Research Hong Kong ( email )

21/F 979 King's Road Quarry Bay
Hong Kong
Hong Kong

City University of Hong Kong (CityUHK) - Department of Information Systems ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

HOME PAGE: http://www.jimmyren.com

Huizhong Ge

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Xiaoyu Wu

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Guan Wang

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Wei Wang

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Stephen Liao

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Register to save articles to
your library

Register

Paper statistics

Downloads
275
rank
108,138
Abstract Views
1,759
PlumX Metrics