Assessing Public Opinions Through Web 2.0: A Case Study on Wal-Mart

The recent advancement of Web 2.0 enables people to exchange their opinions on a variety of topics. Among these discussions, the opinions of employees, customers, and investors are of great interest to companies. Insight into such perspectives can help managers make better decisions on business policies and strategy. However, assessing online opinions is a nontrivial task. The high volume of messages, casual writing style, and the significant amount of noise require the application of sophisticated text mining techniques to digest the data. Previous research has successfully applied sentiment analysis to assess online opinions on specific items and topics. In this research, we propose the integration of topic analysis with sentiment analysis methods to assess the public opinions expressed in forums with diverse topics of discussion. Using a Wal- Mart-related Web forum as an example, we found that combining the two types of analysis can provide us with improved ability to assess public opinions on a company. Through further analysis on one cluster of discussions, several abnormal traffic and sentiment patterns were identified related to Wal-Mart events. The case study validates the propose framework as an IT artifact to assess online public opinion on companies of interest. Our research promotes further efforts to combine topic and sentiment analysis techniques in online research supporting business decision making

Stakeholder and Sentiment Analysis in Web Forums

Web forums offer open and interactive social communication platforms for numerous participants to share information and offer perspectives on a variety of business and social issues with audiences around the world. In addition to facilitating widespread communication, these web forums contain massive amounts of data and represent rich sources of information that can be utilized to advance the understanding of participants and society. In particular, web forums pertaining to firms and their customers, employees, and investors, represent valuable resources for the acquisition of business intelligence. However, web forums represent a complex analytic landscape requiring the development of automated, intelligent, and scalable analytic approaches. The dissertation follows the design science paradigm in management information systems research, and aims to develop and refine approaches to the analysis of web forums, and to apply these analytic approaches to firm-related web forums to derive information that may explain and predict firm stock behavior. The designs of the devised approaches to web forum analysis are informed by the stakeholder theory of the firm, and systemic functional linguistic theory. We introduce and advance a stakeholder approach to the analysis of firm-related web forums, and improve existing approaches to sentiment analysis in web forums. In Chapter 2 we develop and deploy a stakeholder framework to analyze a popular firm-related finance web forum and apply the extracted measures to explain firm stock return, volatility, and trading volume. In Chapter 3 we advance the stakeholder framework and perform dynamic analyses of web forums over time, and compare several feature representations of stakeholders and approaches to sentiment analysis. We deploy the stakeholder framework to analyze several firm-related web forums, and apply the derived measures to predict firm stock return and perform simulated trading of firm stock over a one year period to determine the economic value of the extracted information. Finally, in Chapter 4 we develop approaches to improve the scalability of sentiment analysis across multiple web forums in a collection. Overall the dissertation contributes to the literature on the analysis of web forums, and demonstrates the value of firm-related web forums as sources of business intelligence

Detecting Fake Websites: The Contribution of Statistical Learning Theory

Fake websites have become increasingly pervasive, generating billions of dollars in fraudulent revenue at the expense of unsuspecting Internet users. The design and appearance of these websites makes it difficult for users to manually identify them as fake. Automated detection systems have emerged as a mechanism for combating fake websites, however most are fairly simplistic in terms of their fraud cues and detection methods employed. Consequently, existing systems are susceptible to the myriad of obfuscation tactics used by fraudsters, resulting in highly ineffective fake website detection performance. In light of these deficiencies, we propose the development of a new class of fake website detection systems that are based on statistical learning theory (SLT). Using a design science approach, a prototype system was developed to demonstrate the potential utility of this class of systems. We conducted a series of experiments, comparing the proposed system against several existing fake website detection systems on a test bed encompassing 900 websites. The results indicate that systems grounded in SLT can more accurately detect various categories of fake websites by utilizing richer sets of fraud cues in combination with problem-specific knowledge. Given the hefty cost exacted by fake websites, the results have important implications for e-commerce and online security