Web Site Analysis: A Review and Assessment of Previous Research

The emergence of the World Wide Web as a major communication and transaction channel stresses the preeminent importance of a company\u27s Web site for representing the organization, interacting with customers and conducting transactions. In comparison to other channels, the opportunities for targeting specific market segments are somehow limited, due to the Internet\u27s worldwide reach and predominantly anonymous users. Additionally, an ever-increasing number of customers are going online, which prevents the fine tuning of a site for specific user groups. Therefore, it seems essential that organizations possessing Web presence should be well aware of their site\u27s general functionality and how it is perceived by Internet users. For many years the analysis of Web sites has been one of the major topics for both scholars and practitioners, which led to a huge number of different techniques being used for the evaluation of sites. Furthermore, a variety of different theories and models have been developed which include the effects of Web sites as dependent or independent variables. In this paper, I compare different approaches to Web site analysis and present a classification framework. Numerous examples will be given to illustrate the various dimensions of the framework. Furthermore, benefits and drawbacks of the respective methods will be discussed where applicable. The results provide important insights into the current state of the art of Web analysis and will be supportive for anyone planning to conduct a Web analysis as well as for someone who is interested in getting an overview of the research field

E-mail Analysis for Investigators: Techniques and Implementation

E-mail is a common form of communication in regular use today. As such, it is a normal part of investigating a person or a crime. At present, there are many tools to perform bulk analysis and basic searching, but our research advances the state of the art by applying text mining and unsupervised learning techniques to automate the e-mail analysis process. Our key goals are to group similar e-mails together and to identify the concepts (subjects of discussion) of those e-mail groups. We present several new methods to increase the grouping accuracy: e-mail domain analysis and word pair analysis. We also present a technique for concept analysis. These goals are achieved by integrating our research with the capabilities of Weka, an open-source machine learning suite, and WordNet, a lexical database of the English language. We apply this research to the publicly available Enron e-mail dataset. We verify the results by examining the comparative advantage of each new technique