Another look at automatic text-retrieval systems
Communications of the ACM
A self-organizing semantic map for information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
A corpus-based approach to language learning
A corpus-based approach to language learning
A graphical, self-organizing approach to classifying electronic meeting output
Journal of the American Society for Information Science
Map displays for information retrieval
Journal of the American Society for Information Science
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Exploiting clustering and phrases for context-based information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
CEO and CIO perspectives on competitive intelligence
Communications of the ACM
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Comparing noun phrasing techniques for use with medical digital library tools
Journal of the American Society for Information Science - Special topic issue on digital libraries: part 2
Evaluating the effectiveness of visual user interfaces for information retrieval
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Personalized spiders for web search and analysis
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
MetaSpider: meta-searching and categorization on the Web
Journal of the American Society for Information Science and Technology
Information Retrieval
Self-Organizing Maps
CI Spider: a tool for competitive intelligence on the web
Decision Support Systems
Design and evaluation of a multi-agent collaborative Web mining system
Decision Support Systems - Web retrieval and mining
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track1 - Volume 1
International Journal of Human-Computer Studies
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Journal of Management Information Systems
Visualizing web search results using glyphs: Design and evaluation of a flower metaphor
ACM Transactions on Management Information Systems (TMIS)
A hybrid system for online detection of emotional distress
PAISI'12 Proceedings of the 2012 Pacific Asia conference on Intelligence and Security Informatics
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The World Wide Web presents significant opportunities for business intelligence analysis as it can provide information about a company's external environment and its stakeholders. Traditional business intelligence analysis on the Web has focused on simple keyword searching. Recently, it has been suggested that the incoming links, or backlinks, of a company's Web site (i.e., other Web pages that have a hyperlink pointing to the company of interest) can provide important insights about the company's “online communities.” Although analysis of these communities can provide useful signals for a company and information about its stakeholder groups, the manual analysis process can be very time-consuming for business analysts and consultants. In this article, we present a tool called Redips that automatically integrates backlink meta-searching and text-mining techniques to facilitate users in performing such business intelligence analysis on the Web. The architectural design and implementation of the tool are presented in the article. To evaluate the effectiveness, efficiency, and user satisfaction of Redips, an experiment was conducted to compare the tool with two popular business intelligence analysis methods—using backlink search engines and manual browsing. The experiment results showed that Redips was statistically more effective than both benchmark methods (in terms of Recall and F-measure) but required more time in search tasks. In terms of user satisfaction, Redips scored statistically higher than backlink search engines in all five measures used, and also statistically higher than manual browsing in three measures. © 2007 Wiley Periodicals, Inc.