Abstract
A simple and novel method for generating labeled examples for sentiment analysis is introduced: news stories about publicly traded companies are labeled positive or negative according to price changes of the company stock. It is shown that there are many lexical markers for bad news but none for good news. Overall, learned models based on lexical features can distinguish good news from bad news with accuracy of about 70%. Unfortunately, this result does not yield profits since it works only when stories are labeled according to cotemporaneous price changes but does not work when they are labeled according to subsequent price changes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
5. References
Das, S. and Chen, M. (2001) Yahoo for Amazon: Extracting Market Sentiment from Stock Message Boards. In Proceedings of the 8th Asia Pacific Finance Association Annual Conference (APFA 2001), Bangkok, Thailand.
Fama, E. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance 25, 383–417.
Finn, A. and Kushmerick, N. (2003) Learning to classify documents according to genre. In IJCAI-03 Workshop on Computational Approaches to Style Analysis and Synthesis, Acapulco, Mexico.
Kushal D., Lawrence, S., and Pennock, D. M. (2003) Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the Twelfth International World Wide Web Conference (WWW-2003), 519–528, Budapest, Hungary.
Lavrenko, V., Schmill, M., Lawrie, D., Ogilvie, P., Jensen, D., and Allan, J. (2000) Mining of Concurrent Text and Time Series. In Proceedings of Text Mining Workshop of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 37–44, Boston, MA.
Pang, B., Lee, L. and Vaithyanathan, S. (2002) Thumbs up? Sentiment Classification using Machine Learning Techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), 79–86, Philadelphia, PA.
Seo, Y., Giampapa, J.A., and Sycara, K. (2002) Text Classification for Intelligent Portfolio Management. Technical report CMU-RI-TR-02-14, Robotics Institute, Carnegie Mellon University.
Turney, P. D. (2002) Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of ACL 2002, 417–424, Philadelphia, PA.
Wiebe, J., Bruce, R., Bell, M., Martin, M., and Wilson, T. (2001) A Corpus Study of Evaluative and Speculative Language. In Proceedings of 2nd ACL SIGdial Workshop on Discourse and Dialogue. Aalborg, Denmark.
Wiebe, J., Wilson, T., and Bell, M. (2001) Identifying Collocations for Recognizing Opinions. In Proceedings of ACL 01 Workshop on Collocation. Toulouse, France.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this chapter
Cite this chapter
Koppel, M., Shtrimberg, I. (2006). Good News or Bad News? Let the Market Decide. In: Shanahan, J.G., Qu, Y., Wiebe, J. (eds) Computing Attitude and Affect in Text: Theory and Applications. The Information Retrieval Series, vol 20. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4102-0_22
Download citation
DOI: https://doi.org/10.1007/1-4020-4102-0_22
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-4026-9
Online ISBN: 978-1-4020-4102-0
eBook Packages: Computer ScienceComputer Science (R0)