NDLI: News and Sentiment Analysis of the European Market with a Hybrid Expert Weighting Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Creamer, G.G. Yong Ren Sacamoto, Y. Nickerson, J.V.
Copyright Year	2013
Description	Author affiliation: Stevens Inst. of Technol., Hoboken, NJ, USA (Creamer, G.G.; Yong Ren; Sacamoto, Y.; Nickerson, J.V.)
Abstract	This paper proposes a hybrid human machine system based on an expert weighting algorithm that combines the responses of both humans and machine learning algorithms. The general topic of the paper is the use of the crowd to interpret text, and the power of that interpretation to predict future events. This topic is addressed through an experiment, in which news sentiment is evaluated by crowds and experts in different configurations. Their classifications are used as training sets for machine learning algorithms, including one that weights both machine and human predictions. The testing is done based on Thomson Reuters news stories and the returns of the stocks mentioned right after the stories appear. The hybrid expert weighting algorithm forecasts asset returns similar to the different versions of the trained and crowd groups because it combines the best results of the machine learning algorithms with human answers. The forecast of the expert weighting algorithm does not always show the best performance in comparison with the other learning algorithms, however its performance is very similar to the best algorithm in most cases. From a cognitive perspective, the capacity of the expert weighting algorithm to select dynamically the best expert according to its previous performance is consistent with an evolving collective intelligence: the final decision is a combination of the best individual answers - some of these come from machines, and some from humans.
Starting Page	391
Ending Page	396
File Size	175156
Page Count	6
File Format	PDF
ISBN	9780769551371
DOI	10.1109/SocialCom.2013.61
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Support vector machines Crowdsourcing Machine learning algorithms Text analysis Cognitive modeling Heuristic algorithms Machine learning Prediction algorithms Market research Computational finance Niobium
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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