Abstract. Opinion mining and sentiment analysis are important research areas of Natural Language Processing (NLP) tools and have become viable alternatives for automatically extracting the affective information found in texts. Our aim is to build an NLP model to analyze gamers' sentiments and opinions expressed in a corpus of 9750 game reviews. A Principal Component Analysis using sentiment analysis features explained 51.2 % of the variance of the reviews and provides an integrated view of the major sentiment and topic related dimensions expressed in game reviews. A Discriminant Function Analysis based on the emerging components classified game reviews into positive, neutral and negative ratings with a 55 % accuracy.

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Computer Supported Collaborative Learning (CSCL) environments facilitated by technology have become a viable learning alternative from which valuable data can be extracted and used for advanced analyses centered on evaluating participants' involvement and their interactions. Such automated assessments are implemented within the ReaderBench framework, a Natural Language Processing platform that contains multiple advanced text analysis functionalities. The ReaderBench framework is based on Cohesion Network Analysis from which different sociograms, relying on semantic similarity, are generated in order to reflect interactions between participants. In this paper, we briefly describe the enforced mechanisms used to compare two Math communities, namely an online knowledge building community and an online course.

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