Kai Heinrich

What is the Conversation About?: A Topic-Model-Based Approach for Analyzing Customer Sentiments in Twitter

In Social Commerce customers evolve to be an important information source for companies. Customers use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments about products or discuss their experiences with them. These sentiments can be very important for the development of products or the enhancement of marketing strategies. The research goal is to analyze customer sentiments in Twitter. The first step in the research is the detection of topics in Twitter entries which contain patterns of interest. For the topic detection, the authors use Latent Dirichlet Allocation for topic modeling. The authors found event based topics in the exemplary context of Sonys 3D TV sets. In future work, the authors will implement sentiment analysis algorithms in order to determine sentiments in the entries corresponding to the detected topics.

Effective Visualizations of Energy Consumption in a Feedback System – A Conjoint Measurement Study

Sustainable use of energy is one of the guiding principles of today’s society. But there is a lack of comprehensive analysis solutions for the energy consumption of private households to provide real insights. In order to provide useful information, feedback systems may be the answer. Numerous studies about feedback systems have been conducted so far and each individual component of such a system has been tested. The combination of these components leads to a dashboard for decision support of private households. Within this study the individual components were combined in several configurations and implemented as a prototype dashboard. A Conjoint measurement is used for evaluation and observation of user preferences collected in over 1,000 questionnaires. The result, an evaluated dashboard, combines several effective feedback elements based on user preferences and helps to save energy based on decision support and transparency.