In this paper we propose a system that performs the classification of customer reviews of hotels by means of a sentiment analysis. We elaborate on a process to extract a domainspecific lexicon of semantically relevant words based on a given corpus (Scharl et al., 2003;Pak & Paroubek, 2010). The resulting lexicon backs the sentiment analysis for generating a classification of the reviews. The evaluation of the classification on test data shows that the proposed system performs better compared to a predefined baseline: if a customer review is classified as good or bad the classification is correct with a probability of about 90%.

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Tokenization is commonly understood as the first step of any kind of natural language text preparation. The major goal of this early (pre-linguistic) task is to convert a stream of characters into a stream of processing units called tokens. Beyond the text mining community this job is taken for granted. Commonly it is seen as an already solved problem comprising the identification of word borders and punctuation marks separated by spaces and line breaks. But in our sense it should manage language related word dependencies, incorporate domain specific knowledge, and handle morphosyntactically relevant linguistic specificities. Therefore, we propose rule-based extended tokenization including all sorts of linguistic knowledge (e.g., grammar rules, dictionaries). The core features of our implementation are identification and disambiguation of all kinds of linguistic markers, detection and expansion of abbreviations, treatment of special formats, and typing of tokens including single-and multi-tokens. To improve the quality of text mining we suggest linguistically-based tokenization as a necessary step preceeding further text processing tasks.In this paper, we focus on the task of improving the quality of standard tagging.

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