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Abstract-Using social media tools such as blogs and forums have become more and more popular in recent years. Hence, a huge collection of social media texts from different communities is available for accessing user opinions, e.g., for marketing studies or acceptance research. Typically, methods from Natural Language Processing are applied to social media texts to automatically recognize user opinions. A fundamental component of the linguistic pipeline in Natural Language Processing is Part-of-Speech tagging. Most state-of-the-art Part-of-Speech taggers are trained on newspaper corpora, which differ in many ways from non-standardized social media text. Hence, applying common taggers to such texts results in performance degradation. In this paper, we present extensions to a basic Markov model tagger for the annotation of social media texts. Considering the German standard Stuttgart/Tübinger TagSet (STTS), we distinguish 54 tag classes. Applying our approach improves the tagging accuracy for social media texts considerably, when we train our model on a combination of annotated texts from newspapers and Web comments.

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Availability of ubiquitous wireless services is taken for granted by most of the people. Therefore, network operators have to deploy and to operate large radio networks that, particularly, support broadband services and applications. One effect is a growing cell density, i.e., the number of required base stations increases. On the other hand, there are people that are afraid of getting harmed by electromagnetic radiation emitted by base stations or there are people that just dislike the prevalence of base station antennas. Both types of people's attitude refer to the field of user acceptance. A high deficiency in acceptance might lead to public disputes, political disputes, and negative economical consequences. To counteract such trends preventively, we propose a mathematical optimization model that allows for planning wireless network infrastructure with respect to user acceptance. While keeping technical and economical aspects as primarily considered planning criteria, we propose a multi-objective optimization model that takes into account user acceptance as an objective. Numerical results demonstrate effects of this approach compared to application of a conventional planning model.

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