Studying technology acceptance requires the survey and analysis of user opinions to identify acceptance-relevant factors. In addition to surveys, Web 2.0 poses a huge collection of user comments regarding different technologies. Extracting acceptance-relevant factors and user opinions from such comments requires the application of Natural Language Processing (NLP) methods, particularly Part-Of-Speech (POS) tagging. Applied to typical blog language POS tagging often suffers from high error rates. In this paper, the authors present multiple user-specific studies of blog comments to analyze the relation between blog language and performance of NLP methods.