Intelligent financial news digest system

Authors:
James N. K. Liu;Honghua Dai;Lina Zhou
Affiliations:
Department of Computing, Hong Kong Polytechnic University;School of Information Technology, Deakin University, Australia;Department of Information Systems, University of Maryland, Baltimore County
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

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Abstract

We present an agent-based system Intelligent Financial News Digest System (IFNDS) for analyzing online financial news articles and associated material. The system can abstract, synthesize, digest, and classify the contents, and assesses whether the report is favorable to any company discussed in the reports. It integrates artificial intelligence technologies including traditional information retrieval and extraction techniques for the news analysis. It makes use of keyword statistics and backpropagation training data to identify companies named in reportage whether it is, evaluatively speaking, positive, negative or neutral. The system would be of use to media such as clipping services, media management, advertising, public relations, public interest, and e-commerce professionals and government non-governmental bodies interested in monitoring the media profiles of corporations, products, and issues.