Abstract:
Some articles on Business and Market Intelligence 2.0 from distinguished experts in marketing science, finance, accounting, and computer science, are presented. 'The Phase Transition of Markets and Organizations: The New Intelligence and Entrepreneurial Frontier', characterizes phase transition in markets and organizations as a move from individuals and resources being separate to being together. 'User- Generated Content on Social Media: Predicting New Product Market Success from Online Word of Mouth', explores the predictive validity of various text and sentiment measures of online word of mouth (WOM) for the market success of new products. 'On Data- Driven Analysis of User-Generated Content', discusses data-driven approaches, including content and network analysis that can be used to derive insights and characterize user-generated content from companies and other organizations.
Abstract:
Two studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of five computational models of online document retrieval. These models were then incorporated into the design of an "intelligent" document-based retrieval system. Following a discussion of this system, we discuss the broader implications of our research for the design of information retrieval systems. © 1991.
Abstract:
Analyzing authorship of online texts is an important analysis task in security-related areas such as cybercrime investigation and counter-terrorism, and in any field of endeavor in which authorship may be uncertain or obfuscated. This paper presents an automated approach for authorship analysis using machine learning methods, a robust stylometric feature set, and a series of visualizations designed to facilitate analysis at the feature, author, and message levels. A testbed consisting of 506,554 forum messages, in English and Arabic, from 14,901 authors was first constructed. A prototype portal system was then developed to support feasibility analysis of the approach. A preliminary evaluation to assess the efficacy of the text visualizations was conducted. The evaluation showed that task performance with the visualization functions was more accurate and more efficient than task performance without the visualizations. © 2013 IEEE.
Abstract:
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 and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91% on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments. © 2008 ACM.
