Analyse der Meinungsentwicklung in Online-Foren - Konzept und Fallstudie - Kaiser, Carolin; Bodendorf, Freimut (Virtual enterprises communities & social networks / Workshop GeNeMe '10 - Gemeinschaften in Neuen Medien, 2010)
WiSo-Import:2015-04-17:700
Keywords: Innovation

Analyse von Meinungen in sozialen Netzwerken des Web 2.0 - Kaiser, Carolin (Wirtschaftsinformatik Proceedings 2009, 2009)
WiSo2-Import:2015-10-02:446

Analyzing Opinion Formation in Online Social Networks - Mining Services for Online Market Research - Kaiser, Carolin; Kröckel, Johannes; Bodendorf, Freimut (Proceedings of the 2011 Annual SRII Global Conference, 2011)
Networking via the Internet is becoming very popular. Many people meet up in online social networks where they form friendships, share the same interests and discuss several topics. By interacting with each other, people influence one another's opinions. Analyzing opinion formation in online social networks yields significant information for market research. This paper introduces mining services which explain and predict opinion formation of the network members on the basis of their social relationships by combining methods from text mining and swarm intelligence. The approach is illustrated by taking an online gaming community as an example. © 2011 IEEE.
WiSo-Import:2015-04-17:712
Keywords: Innovation

Ant-based Simulation of Opinion Spreading in Online Social Networks - Kaiser, Carolin; Kröckel, Johannes; Bodendorf, Freimut (2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2010)
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. © 2010 IEEE.
WiSo-Import:2015-04-17:699
Keywords: Innovation

Are Humans like Ants? - Analyzing Collective Opinion Formation in Online Discussions - Kaiser, Carolin; Piazza, Alexander; Kröckel, Johannes; Bodendorf, Freimut (Proceedings of the Third IEEE International Conference on Social Computing, 2011)
Web 2.0 platforms give people more power over the way they share information and exchange opinions. The increase in social interactivity leads to the emerging of self-organized communities where members form their opinions via social swarming. This paper introduces a new approach which enables predicting and explaining the process of collective opinion formation by social swarming. A computational model for simulating collective opinion formation is derived from the ant colony metaheuristic and applied to an exemplary online community where the members'opinions are identified via text mining. For validation purposes, this approach is compared to three other approaches. © 2011 IEEE.
WiSo2-Import:2015-10-02:588
Keywords: Ant colony optimization; Collective intelligent behavior; Group dynamics; Online communities; Opinion formation; Swarm intelligence; Text mining

Combining Text Mining and Data Mining For Gaining Valuable Knowledge from Online Reviews - Kaiser, Carolin (IADIS International Journal on WWW/Internet, 2009)
WiSo2-Import:2015-09-25:466

Detecting Opinion Leaders and Trends in Online Communities - Bodendorf, Freimut; Kaiser, Carolin (2010 Fourth International Conference on Digital Society, 2010)
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. © 2010 IEEE.
WiSo-Import:2015-04-17:686
Keywords: Innovation; Forschungsbericht 2010

Detecting Opinion Leaders and Trends in Online Social Networks - Bodendorf, Freimut; Kaiser, Carolin (Proceedings of the 2nd Workshop on Social Web Search and Mining, 2009)
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. Copyright 2009 ACM.
WiSo2-Import:2015-10-02:461
Keywords: Leaders; Opinion mining; Social network analysis; Trends

Discovering Critical Situations in Online Social Networks - A Neuro Fuzzy Approach to Alert Marketing Managers - Kaiser, Carolin; Bodendorf, Freimut; Schlick, Sabine (Proceedings of the 2nd International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2010)
More and more people are exchanging their opinions in online social networks and influencing each other. It is crucial for companies to observe opinion formation concerning their products. Thus, risks can be recognized early on and counteractive measures can be initiated by marketing managers. A neuro fuzzy approach is presented which allows the detection of critical situations in the process of opinion formation and the alerting of marketing managers. Rules for identifying critical situations are learned on the basis of the opinions of the network members, the influence of the opinion leaders and the structure of the network. The opinions and characteristics of the network are identified by text mining and social network analysis. The approach is illustrated by an exemplary application.
WiSo-Import:2015-04-17:701
Keywords: Innovation

Entscheidungsunterstützung zur Meinungsbeeinflussung in Webcommunitys - Kaiser, Carolin (HMD : Praxis der Wirtschaftsinformatik, 2011)
WiSo2-Import:2015-09-25:526

Frühwarnsystem zur Identifikation kritischer Situationen der Meinungsbildung im Web 2.0 - Kaiser, Carolin; Schlick, Sabine (Wirtschaftsinformatik Proceedings 2011, 2011)
WiSo2-Import:2015-10-02:492

Meinungsanalyse in Onlinenetzwerken mittels Schwarmintelligenz - Kaiser, Carolin; Kröckel, Johannes (Informatik-Spektrum, 2010)
WiSo2-Import:2015-09-25:476

Mining Consumer Dialog in Online Forums - Kaiser, Carolin; Bodendorf, Freimut (Internet Research, 2012)
Purpose: The paper's 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 initiate appropriate marketing actions. Originality/value: This work is one of the first studies that combine communication content, relationships and dialog acts for analyzing opinion formation. © Emerald Group Publishing Limited.
WiSo-Import:2015-03-26:4537
Keywords: Consumer behaviour; Data analysis; Digital communication systems; Information exchange; Internet; Social networks; Text mining

Mining Customer Opinions on the Internet - A Case Study in the Automotive Industry - Bodendorf, Freimut; Kaiser, Carolin (2010 Third International Conference on Knowledge Discovery and Data Mining, 2010)
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 automated 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. © 2010 IEEE.
WiSo-Import:2015-04-17:685
Keywords: Innovation

Mining Patient Experiences on Web 2.0 - A Case Study in the Pharmaceutical Industry - Kaiser, Carolin; Bodendorf, Freimut (2012 Annual SRII Global Conference, 2012)
An increasing number of patients and family members interact online and exchange their experiences with diseases and therapies. The huge amount of online health data represents a rich source of knowledge pharmaceutical companies. The analysis of this data enables the identification of strengths and weaknesses of their drugs. An approach is presented which allows the extraction and analysis of patient experiences with drugs expressed in online reviews by combining methods coming from text mining and data mining. The approach is exemplarily applied to a data set comprising patients' experiences with smoking deterrents.
Keywords: Health 2.0; Opinion Mining; Text Mining; Data Mining; Patient Experiences; Internet

Monitoring Opinions in Online Forums – A Case Study from the Sports Industry - Kaiser, Carolin; Bodendorf, Freimut (International Journal of Information and Education Technology, 2012)
WiSo-Import:2015-03-26:4538

Opinion and Relationship Mining in Online Forums - Kaiser, Carolin; Bodendorf, Freimut (Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 2009)
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. © 2009 IEEE.
WiSo2-Import:2015-10-02:456

Opinion Mining im Web 2.0 - Konzept und Fallbeispiel - Kaiser, Carolin (HMD : Praxis der Wirtschaftsinformatik, 2009)
WiSo2-Import:2015-09-25:455

Simulating the spread of opinions in online social networks when targeting opinion leaders - Kaiser, Carolin; Kröckel, Johannes; Bodendorf, Freimut (Information Systems and E-Business Management, 2012)
WiSo-Import:2015-03-26:4519
Keywords: Opinion mining; Diffusion; Social network analysis; Influence; Opinion leaders; Ant algorithms

Swarm Intelligence for Analyzing Opinions in Online Communities - Kaiser, Carolin; Kröckel, Johannes; Bodendorf, Freimut (2010 43rd Hawaii International Conference on System Sciences, 2010)
Keywords: artificial intelligence; data mining; evolutionary computation; social networking (online); Web 2.0 platforms; evolutionary process; online communities; opinion formation; social interactions; social networking; social swarming; swarm intelligence; text mining; Ant colony optimization; Computational modeling; Data mining; Humans; Internet; Online Communities/Technical Collaboration; Organizing; Particle swarm optimization; Social network services; Text mining

Warning system for online market research - Identifying critical situations in online opinion formation - Kaiser, Carolin; Schlick, Sabine; Bodendorf, Freimut (Knowledge-Based Systems, 2011)
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. © 2011 Elsevier B.V. All rights reserved.
WiSo-Import:2015-03-26:713
Keywords: Innovation