Analyzing customer sentiments in microblogs – A topic-model-based approach for Twitter datasets

In the Social Commerce customers evolve to an important information source for companies. The customers use communication platforms of the Web 2.0, for example Twitter, in order to express their opinions about products or discuss their experiences with them. These opinions can be very important for the development of products or the product range of a company. Our approach enables a company viewing opinions about its products which are published using the microblogging service Twitter. A first step in our research progress is detecting topics in a specific context. In a further step the entries corresponding to these topics has to be analyzed for opinions. For topic detection we use topic modeling with the Latent Dirichlet Allocation. In our paper we found event-based topics in the context of Sony’s 3D TV sets. In future work we are able to implement Opinion Mining algorithms to determine sentiments in the entries corresponding to the detected topics

Idea Mining – Text Mining Supported Knowledge Management for Innovation Purposes

Following the emergence of Social Media and the increasing willingness of customers to share thoughts, ideas, and experiences companies are trying to capitalize on such activities. Due to the vast amount of user-generated content, manual analysis and interpretation will not meet the demands of companies in highly competitive environments. Based on an integrative process model, which describes the process of idea generation, we outline a BPMN-based path that allows companies to steer user participation and the application of Text Mining methods to gain valuable ideas for innovative products. Our approach also illustrates the Knowledge Management perspective supporting the customers during idea generation. In order to demonstrate the applicability of our model we finally depict the whole process utilizing Dell’s IdeaStor

Social Business Intelligence: a Literature Review and Research Agenda

The domains of Business Intelligence (BI) and social media have meanwhile become significant research fields. While BI aims at supporting an organization’s decisions by providing relevant analytical data, social media is an emerging source of personal and individual knowledge, opinion, and attitudes of stakeholders. For a while, a convergence of the two domains can be observed in real-world implementations and research, resulting in concepts like social BI. Many research questions still remain open – or even worse – are not yet formulated. Therefore, the paper aims at articulating a research agenda for social BI. By means of a literature review we systematically explored previous work and developed a framework. It contrasts social media characteristics with BI design areas and is used to derive the social BI research agenda. Our results show that the integration of social media (data) into a BI system has impact on almost all BI design objects

High Performance Twitter Sentiment Analysis Using CUDA Based Distance Kernel on GPUs

Sentiment analysis techniques are widely used for extracting feelings of users in different domains such as social media content, surveys, and user reviews. This is mostly performed by using classical text classification techniques. One of the major challenges in this field is having a large and sparse feature space that stems from sparse representation of texts. The high dimensionality of the feature space creates a serious problem in terms of time and performance for sentiment analysis. This is particularly important when selected classifier requires intense calculations as in k-NN. To cope with this problem, we used sentiment analysis techniques for Turkish Twitter feeds using the NVIDIA’s CUDA technology. We employed our CUDA-based distance kernel implementation for k-NN which is a widely used lazy classifier in this field. We conducted our experiments on four machines with different computing capacities in terms of GPU and CPU configuration to analyze the impact on speed-up

An investigation into customer perception and behaviour through social media research – an empirical study of the United Airline overbooking crisis

Airlines have been adopting yield management to optimise the perishable seat control problem and overbooking is a common strategy. This study outlines the connections between yield management, crises, and crisis communication. Using big data captured on a social media platform, this study aims to combine traditional yield management with emerging social big data analytics. As part of this, we use the twitter data on the 2017 United Airline (UA) to analyse the overbooking crisis. Our findings shed light on the importance of a more effective orchestration of yield management to avoid the escalation of crises during crisis communication phases

Fostering parent–child dialog through automated discussion suggestions

The development of early literacy skills has been critically linked to a child’s later academic success. In particular, repeated studies have shown that reading aloud to children and providing opportunities for them to discuss the stories that they hear is of utmost importance to later academic success. CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills by supporting parents in shared reading with their children through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and, in combination with a common sense knowledge base, leverages this information to produce suggestions. Because of the unique challenges presented by our application, the suggestion generation method relies on a novel topic modeling method that is based on semantic graph topology. We conducted a user study in which we compared how delivering suggestions generated by our approach compares to expert-crafted suggestions. Our results show that our system can successfully improve engagement and parent–child reading practices in the absence of a literacy expert’s tutoring.National Science Foundation (U.S.) (Award Number 1117584

Idea Mining: Wissensmanagement und Text Mining im Innovationsprozess

Motiviert durch den Erfolg des Web 2.0 und Social Media in vielen Bereichen des öffentlichen Lebens und der damit verbundenen Open-Innovation-Bewegung, die Kunden aktiv in den Innovationsprozess einbezieht, schlägt dieser Beitrag eine Integration von Wissensmanagement und Text Mining zur Verbesserung dieses Innovationsprozesses vor. Durch den beschriebenen Ansatz werden Kunden nicht nur motiviert, ihre Ideen und Bedürfnisse auf webbasierten Kommunikationsplattformen preiszugeben, sondern die entstehenden, textbasierten Daten können automatisiert ausgewertet und zur zielgerichteten und zeitnahen Weiterentwicklung der Produkte eingesetzt werden. Anhand zweier Anwendungsszenarien aus der Praxis werden das resultierende Prozessmodell dargestellt und dessen Potenziale veranschaulicht.:1 Einführung 1.1 Motivation 1.2 Forschungsziel 2 Beiträge im Forschungsfeld 3 Kundenorientierte Innovation 3.1 Der Innovationsprozess 3.2 Herausforderungen der Kundenintegration 4 Wissensmanagement 4.1 Anwendungspotenziale im Web 2.0 4.2 Anwendungspotenziale bei der Ideenfindung 5 Text Mining 5.1 Zielstellung und Datenquellen 5.2 Datenvorverarbeitung 5.3 Text-Mining-Verfahren und Anwendung 6 Der erweiterte Innovationsprozess 6.1 Integriertes Prozessmodell 6.2 Anwendungsszenarien 6.2.1 Dell’s IdeaStorm 6.2.2 My Starbucks Idea 7 Fazit und Ausblick Literaturverzeichni