In this paper, we report on three projects in which we are applying natural language processing techniques to analyse video game reviews. We present our process, techniques, and progress for extracting and analysing player reviews from the gaming platform Steam. Analysing video game reviews presents great opportunity to assist players to choose games to buy, to help developers to improve their games, and to aid researchers in further understanding player experience in video games. With limited previous research that specifically focuses on game reviews, we aim to provide a baseline for future research to tackle some of the key challenges. Our work shows promise for using natural language processing techniques to automatically identify features, sentiment, and spam in video game reviews on the Steam platform. CCS CONCEPTS • Computing methodologies → Natural language processing; • Software and its engineering → Interactive games; • Applied computing → Computer games.

The potential value of online reviews has led to more and more spam reviews appearing on the web. These spam reviews are widely distributed, harmful, and difficult to identify manually. In this paper, we explore and implement generalised approaches for identifying online deceptive spam game reviews from Steam. We analyse spam game reviews and present and validate some techniques to detect them. In addition, we aim to identify the unique features of game reviews and to create a labelled game review dataset based on different features. We were able to create a labelled dataset that can be used to identify spam game reviews in future research. Our method resulted in 5,021 of the 33,450 unlabelled Steam reviews being labelled as spam reviews, or approximately 15%. This falls within the expected range of 10-20% and maps to the Yelp figures of 14-20% of reviews are spam.CCS Concepts: • Computing methodologies → Natural language processing; • Software and its engineering → Interactive games; • Applied computing → Computer games.