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The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English(More)
One of the problems often associated with online anonymity is that it hinders social accountability, as substantiated by the high levels of cybercrime. Although identity cues are scarce in cyberspace, individuals often leave behind textual identity traces. In this study we proposed the use of stylometric analysis techniques to help identify individuals(More)
The speed, ubiquity, and potential anonymity of Internet media - email, Web sites, and Internet forums - make them ideal communication channels for militant groups and terrorist organizations. Analyzing Web content has therefore become increasingly important to the intelligence and security agencies that monitor these groups. Authorship analysis can assist(More)
Ahmed AbbAsi is a Professor in the Sheldon b. Lubar School of business at the university of Wisconsin–Milwaukee. he received his Ph.D. in Management Information Systems from the university of Arizona and an MbA and b.S. in Information Technology from Virginia Tech. his research interests include application of text mining and information visualization(More)
A major concern when incorporating large sets of diverse n-gram features for sentiment classification is the presence of noisy, irrelevant, and redundant attributes. These concerns can often make it difficult to harness the augmented discriminatory potential of extended feature sets. We propose a rule-based multivariate text feature selection method called(More)
Content analysis of computer-mediated communication (CMC) is important for evaluating the effectiveness of electronic communication in various organizational settings. CMC text analysis relies on systems capable of providing suitable navigation and knowledge discovery functionalities. However, existing CMC systems focus on structural features, with little(More)
Analysis of affective intensities in computer-mediated communication is important in order to allow a better understanding of online users' emotions and preferences. Despite considerable research on textual affect classification, it is unclear which features and techniques are most effective. In this study, we compared several feature representations for(More)
5 interests include development and evaluation of technologies for enhanced analysis of computer-mediated communication and improved online security. research interests include fraud detection, web mining for business intelligence, and time series forecasting. and more than 200 journal articles covering topics such as digital libraries, data/text/web(More)
As a result of growing misuse of online anonymity, researchers have begun to create visualization tools to facilitate greater user accountability in online communities. In this study we created an authorship visualization called Writeprints that can help identify individuals based on their writing style. The visualization creates unique writing style(More)