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Natural language processing (NLP) is a field of computer science and linguistics devoted to creating computer systems that use human (natural) language as input and/or output. The authors propose that NLP can also be used for game studies research. In this article, the authors provide an overview of NLP and describe some research possibilities that can be explored using NLP tools and techniques. The authors discuss these techniques by performing three different types of NLP analyses of a significant corpus of online videogame reviews: (a) By using techniques such as word and syllable counting, the authors analyze the readability of professionally written game reviews, finding that, across a variety of indicators, game reviews are written for a secondary education reading level; (b) the authors analyze hundreds of thousands of user-submitted game reviews using part-of-speech tagging, parsing, and clustering to examine how gameplay is described. The findings of this study in this area highlight the primary aesthetics elements of gameplay according to the general public of game players; and (c) the authors show how sentiment analysis, or the classification of opinions and feelings based on the words used in a text and the relationship between those words, can be used to explore the circumstances in which certain negatively charged words may be used positively and for what reasons in the domain of videogames. The authors conclude with ideas for future research, including how NLP can be used to complement other avenues of inquiry.

One of the difficulties in using Folksonomies in computational systems is tag ambiguity: tags with multiple meanings. This paper presents a novel method for building Folksonomy tag ontologies in which the nodes are disambiguated. Our method utilizes a clustering algorithm called DSCBC, which was originally developed in Natural Language Processing (NLP), to derive committees of tags, each of which corresponds to one meaning or domain. In this work, we use Wikipedia as the external knowledge source for the domains of the tags. Using the committees, an ambiguous tag is identified as one which belongs to more than one committee. Then we apply a hierarchical agglomerative clustering algorithm to build an ontology of tags. The nodes in the derived ontology are disambiguated in that an ambiguous tag appears in several nodes in the ontology, each of which corresponds to one meaning of the tag. We evaluate the derived ontology for its ontological density (how close similar tags are placed), and its usefulness in applications, in particular for a personalized tag retrieval task. The results showed marked improvements over other approaches.

In this paper we present a modified hierarchical agglomerative clustering algorithm for building tag ontologies for social tagging systems. The modified algorithm first uses a clustering algorithm called Domain Similarity Clustering By Committee (DSCBC) (Tomuro et al. 2007) to derive a set of tag committees. We apply DSCBC to the tags entered by the users of social tagging systems and derive (un-ambiguous) committees of tags.  Using the committees, a tag ontology is constructed in which an ambiguous tag is separated into multiple, disambiguated tags/nodes. Then a tag profile of a given user is matched against the ontology, and an ontological profile of the user is created.  Finally a preference vector is fed into the (modified) FolkRank algorithm (Hotho et al. 2006a), and the web resources ordered based on the user's preferences are returned.  We run our system on the data from two social tagging systems and compare the results with other algorithms. The results showed our algorithm achieved marked improvements over other algorithms.