Carolin Kaiser

Detecting opinion leaders and trends in online social networks

Today, online social networks in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as blogs, wikis, and forums where for example consumers can disseminate data about products and manufacturers. This data provides an abundance of information on personal experiences and opinions which are extremely relevant for companies and sales organizations. A new approach based on text mining and social network analysis is presented which allows detecting opinion leaders and opinion trends. This allows getting a better understanding of the opinion formation. The overall concept is presented and illustrated by an example.

Warning system for online market research - Identifying critical situations in online opinion formation

More and more consumers are relying on online opinions when making purchasing decisions. For this reason, companies must have knowledge of the actual standing of their products on the Web. A warning system for online market research is being proposed which allows the identification of critical situations in online opinion formation. When critical situations are detected, warnings are subsequently sent to marketing managers and thus allowing marketers the ability to initiate preventive measures. The warning system operates on a knowledge base which contains product-related success values, online opinions and patterns of social interactions. This knowledge is acquired using methods coming from information extraction, text mining and social network analysis. Based on this knowledge the warning system judges situations accordingly. For this purpose, a neuro-fuzzy approach is chosen which learns linguistic rules from data. These rules are employed to estimate future situations. The warning system is applied to two scenarios and yields good results. An evaluation shows that all components of the warning system outperform alternative methods.

Mining consumer dialog in online forums

Purpose – The papers aim is to mine and analyze opinion formation on the basis of consumer dialogs in online forums.Design/methodology/approach – The study identifies opinions, communication relationships, and dialog acts of forum users using different text mining methods. Utilizing this data, social networks can be derived and analyzed to detect influential users and opinion tendencies. The approach is applied to sample online forums discussing the iPhone.Findings – Combining text mining and social network analysis enables the study of opinion formation and yields encouraging results. Out of the four methods employed for text mining, support vector machines performed best.Research limitations/implications – The data set applied here is fairly small. More threads on different products will be considered in future work to improve validation.Practical implications – The approach represents a valuable instrument for online market research. It enables companies to recognize opportunities and risks and to ini...

Detecting Opinion Leaders and Trends in Online Communities

Today, online communities in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as blogs, wikis, and forums where for example consumers can disseminate data about products and manufacturers. This data provides an abundance of information on personal experiences and opinions which are extremely relevant for companies and sales organizations. Subjects of postings can be partly retrieved by state of the art text mining techniques. A much more challenging task is to detect factors influencing the evolvement of opinions within the social network. For such a kind of trend scouting you have to take into account the relationships among the community members. Social network analysis helps to explain social behavior of linked persons by providing quantitative measures of social interactions. A new approach based on social network analysis is presented, which allows detecting opinion leaders and opinion trends. This leads a better understanding of opinion formation. The overall concept based on text mining and social network analysis is introduced. An example is given which illustrates the analysis process.

Swarm Intelligence for Analyzing Opinions in Online Communities

Web 2.0 platforms change the collaboration within online communities. A new way of organizing and opinion exchanging derives from increased social interactions and networking among community members. These members join together in self-organizing groups where opinions are forming by social swarming. Explaining and predicting the evolutionary process of opinion formation by social swarming is not only a powerful instrument for opinion research but also a great challenge. A new approach is presented which enables the recognition of opinions of swarm members and the analysis of opinion formation in the overall swarm by combining methods from text mining and swarm intelligence. The concept is illustrated by an example.

Opinion and Relationship Mining in Online Forums

The Internet contains an increasing number of online forums where consumers exchange product opinions. It is important for companies to know how consumers judge their products and how these opinions are spread by interactions throughout online forums. With this knowledge it is possible to recognize risks and chances. However, classical opinion research is very time consuming and only possible to a certain extent. This paper introduces a system which automatically extracts opinions and communication relationships in forums by text mining and identifies influential users and trends by social network analysis.

Opinion Mining im Web 2.0 — Konzept und Fallbeispiel

ZusammenfassungDas Internet wandelt sich zunehmend von einer statischen zu einer interaktiven Plattform. Kundendiskussionen im Web 2.0 stellen für Unternehmen eine reichhaltige Informationsquelle dar. Eine manuelle Analyse des Web 2.0 ist jedoch zeitaufwendig. In diesem Beitrag wird ein Opinion-Mining-Konzept vorgestellt, das durch den Einsatz von Text Mining eine automatisierte Extraktion, Aggregation und Analyse von Kundenmeinungen zu Produkten erlaubt. Somit können Stärken und Schwächen von Produkten erkannt und Ansatzpunkte für die Produktgestaltung und Verkaufsförderung gewonnen werden. Anwendung und Nutzen des Konzeptes werden an einem Fallbeispiel der Automobilbranche aufgezeigt.

Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures

Collective intelligence (CI) is a property of groups that emerges from the coordination and collaboration of members and predicts group performance on a wide range of tasks. Previous studies of CI have been conducted with lab-based groups in the USA. We introduce a new standardized online battery to measure CI and demonstrate consistent emergence of a CI factor across three different studies despite broad differences in (a) communication media (face-to-face vs online), (b) group contexts (short-term ad hoc groups vs long-term groups) and (c) cultural settings (US, Germany, and Japan). In two of the studies, we also show that CI is correlated with a groups performance on more complex tasks. Consequently, the CI metric provides a generalizable performance measure for groups that is robust to broad changes in media, context, and culture, making it useful for testing the effects of general-purpose collaboration technologies intended to improve group performance.

Ant-Based Simulation of Opinion Spreading in Online Social Networks

An increasing number of people are socializing within online networks. By means of interaction, network members influence one another’s opinion. For companies, it is important to know how opinions spread throughout networks in order to be able to take appropriate marketing actions. A new approach is presented which simulates the spread of opinions within online social networks. The principles of opinion formation are first revealed by ant mining algorithms coming from swarm intelligence and then applied to simulate the spread of opinions.

Mining Customer Opinions on the Internet - A Case Study in the Automotive Industry

The Internet is increasingly changing from a medium of distribution to a platform of interaction. Customer discussions in Web 2.0 are a valuable source of information for companies. An opinion mining approach is presented which allows an auto-mated extraction, aggregation, and analysis of customer opinions on products by using text mining. Thus, strengths and weaknesses judged by customers can be detected at an early stage and starting points for product design and marketing can be identified. The application of the approach is illustrated by a case study coming from the automotive industry.