@article{markos2021sentiment,

title = {What Causes Wrong Sentiment Classifications of Game Reviews?},

author = {Markos Viggiato and Dayi Lin and Abram Hindle and Cor-Paul Bezemer},

year  = {2021},

date = {2021-04-05},

urldate = {2021-04-05},

journal = {IEEE Transactions on Games},

pages = {1--14},

institution = {University of Alberta},

abstract = {Sentiment analysis is a popular technique to identify the sentiment of a piece of text. Several different domains have been targeted by sentiment analysis research, such as Twitter, movie reviews, and mobile app reviews. Although several techniques have been proposed, the performance of current sentiment analysis techniques are still far from acceptable, mainly when applied in domains on which they were not trained. In addition, the causes of wrong classifications are not clear. In this paper, we study how sentiment analysis performs on game reviews. We first report the results of a large scale empirical study on the performance of widely-used sentiment classifiers on game reviews. Then, we investigate the root causes for the wrong classifications and quantify the impact of each cause on the overall performance. We study three existing classifiers: Stanford CoreNLP, NLTK, and SentiStrength. Our results show that most classifiers do not perform well on game reviews, with the best one being NLTK (with an AUC of 0.70). We also identified four main causes for wrong classifications, such as reviews that point out advantages and disadvantages of the game, which might confuse the classifier. The identified causes are not trivial to be resolved and we call upon sentiment analysis and game researchers and developers to prioritize a research agenda that investigates how the performance of sentiment analysis of game reviews can be improved, for instance by developing techniques that can automatically deal with specific game-related issues of reviews (e.g., reviews with advantages and disadvantages). Finally, we show that training sentiment classifiers on reviews that are stratified by the game genre is effective.},

keywords = {Computer games, Natural language processing, Sentiment analysis, Steam},

pubstate = {published},

tppubtype = {article}

}

@article{Lin2019videos,

title = {Identifying Gameplay Videos that Exhibit Bugs in Computer Games},

author = {Dayi Lin and Cor-Paul Bezemer and Ahmed E. Hassan},

year  = {2019},

date = {2019-05-21},

urldate = {2019-05-21},

journal = {Empirical Software Engineering Journal (EMSE)},

abstract = {With the rapid growing market and competition in the gaming industry, it is challenging to develop a successful game, making the quality of games very important. To improve the quality of games, developers commonly use gamer-submitted bug reports to locate bugs in games. Recently, gameplay videos have become popular in the gaming community. A few of these videos showcase a bug, offering developers a new opportunity to collect context-rich bug information.

In this paper, we investigate whether videos that showcase a bug can automatically be identified from the metadata of gameplay videos that are readily available online. Such bug videos could then be used as a supplemental source of bug information for game developers. We studied the number of gameplay videos on the Steam platform, one of the most popular digital game distribution platforms, and the difficulty of identifying bug videos from these gameplay videos. We show that naïve approaches such as using keywords to search for bug videos are time-consuming and imprecise. We propose an approach which uses a random forest classifier to rank gameplay videos based on their likelihood of being a bug video. Our proposed approach achieves a precision that is 43% higher than that of the naïve keyword searching approach on a manually labelled dataset of 96 videos. In addition, by evaluating 1,400 videos that are identified by our approach as bug videos, we calculated that our approach has both a mean average precision at 10 and a mean average precision at 100 of 0.91. Our study demonstrates that it is feasible to automatically identify gameplay videos that showcase a bug.},

keywords = {Bug report, Computer games, Gameplay videos, Steam},

pubstate = {published},

tppubtype = {article}

}

@article{Lin2018reviews,

title = {An Empirical Study of Game Reviews on the Steam Platform},

author = {Dayi Lin and Cor-Paul Bezemer and Ying Zou and Ahmed E. Hassan},

year  = {2018},

date = {2018-06-15},

urldate = {2018-06-15},

journal = {Empirical Software Engineering Journal (EMSE)},

abstract = {The steadily increasing popularity of computer games has led to the rise of a multi-billion dollar industry. Due to the scale of the computer game industry, developing a successful game is challenging. In addition, prior studies show that gamers are extremely hard to please, making the quality of games an important issue. Most online game stores allow users to review a game that they bought. Such reviews can make or break a game, as other potential buyers often base their purchasing decisions on the reviews of a game. Hence, studying game reviews can help game developers better understand user concerns, and further improve the user-perceived quality of games.

In this paper, we perform an empirical study of the reviews of 6,224 games on the Steam platform, one of the most popular digital game delivery platforms, to better understand if game reviews share similar characteristics with mobile app reviews, and thereby understand whether the conclusions and tools from mobile app review studies can be leveraged by game developers. In addition, new insights from game reviews could possibly open up new research directions for research of mobile app reviews. We first conduct a preliminary study to understand the number of game reviews and the complexity to read through them. In addition, we study the relation between several game-specific characteristics and the fluctuations of the number of reviews that are received on a daily basis. We then focus on the useful information that can be acquired from reviews by studying the major concerns that users express in their reviews, and the amount of play time before players post a review. We find that game reviews are different from mobile app reviews along several aspects. Additionally, the number of playing hours before posting a review is a unique and helpful attribute for developers that is not found in mobile app reviews. Future longitudinal studies should be conducted to help developers and researchers leverage this information. Although negative reviews contain more valuable information about the negative aspects of the game, such as mentioned complaints and bug reports, developers and researchers should also not ignore the potentially useful information in positive reviews. Our study on game reviews serves as a starting point for other game review researchers, and suggests that prior studies on mobile app reviews may need to be revisited.},

keywords = {Computer games, Game reviews, Steam},

pubstate = {published},

tppubtype = {article}

}

@article{Lin16eag,

title = {An Empirical Study of Early Access Games on the Steam Platform},

author = {Dayi Lin and Cor-Paul Bezemer and Ahmed E. Hassan},

year  = {2018},

date = {2018-04-01},

urldate = {2018-04-01},

journal = {The Empirical Software Engineering Journal (EMSE)},

volume = {23},

number = {2},

pages = {771--799},

publisher = {Springer},

abstract = {“Early access” is a release strategy for software that allows consumers to purchase an unfinished version of the software. In turn, consumers can influence the software development process by giving developers early feedback. This early access model has become increasingly popular through digital distribution platforms, such as Steam which is the most popular distribution platform for games. The plethora of options offered by Steam to communicate between developers and game players contribute to the popularity of the early access model. The model is considered a success by the game development community as several games using this approach have gained a large user base (i.e., owners) and high sales. On the other hand, the benefits of the early access model have been questioned as well.

In this paper, we conduct an empirical study on 1,182 Early Access Games (EAGs) on the Steam platform to understand the characteristics, advantages and limitations of the early access model. We find that 15% of the games on Steam make use of the early access model, with the most popular EAG having as many as 29 million owners. 88% of the EAGs are classified by their developers as so-called “indie” games, indicating that most EAGs are developed by individual developers or small studios.

We study the interaction between players and developers of EAGs and the Steam platform. We observe that on the one hand, developers update their games more frequently in the early access stage. On the other hand, the percentage of players that review a game during its early access stage is lower than the percentage of players that review the game after it leaves the early access stage. However, the average rating of the reviews is much higher during the early access stage, suggesting that players are more tolerant of imperfections in the early access stage. The positive review rate does not correlate with the length or the game update frequency of the early access stage.

Based on our findings, we suggest game developers to use the early access model as a method for eliciting early feedback and more positive reviews to attract additional new players. In addition, our findings suggest that developers can determine their release schedule without worrying about the length of the early access stage and the game update frequency during the early access stage.},

keywords = {Computer games, Early access games, Steam},

pubstate = {published},

tppubtype = {article}

}

@article{Lin16urgent,

title = {Studying the Urgent Updates of Popular Games on the Steam Platform},

author = {Dayi Lin and Cor-Paul Bezemer and Ahmed E. Hassan},

year  = {2017},

date = {2017-08-01},

urldate = {2017-08-01},

journal = {The Empirical Software Engineering Journal (EMSE)},

volume = {22},

number = {4},

pages = {2095--2126},

publisher = {Springer},

abstract = {The steadily increasing popularity of computer games has led to the rise of a multi-billion dollar industry. This increasing popularity is partly enabled by online digital distribution platforms for games, such as Steam. These platforms offer an insight into the development and test processes of game developers. In particular, we can extract the update cycle of a game and study what makes developers deviate from that cycle by releasing so-called urgent updates.

An urgent update is a software update that fixes problems that are deemed critical enough to not be left unfixed until a regular-cycle update. Urgent updates are made in a state of emergency and outside the regular development and test timelines which causes unnecessary stress on the development team. Hence, avoiding the need for an urgent update is important for game developers. We define urgent updates as 0-day updates (updates that are released on the same day), updates that are released faster than the regular cycle, or self-admitted hotfixes.

We conduct an empirical study of the urgent updates of the 50 most popular games from Steam, the dominant digital game delivery platform. As urgent updates are reflections of mistakes in the development and test processes, a better understanding of urgent updates can in turn stimulate the improvement of these processes, and eventually save resources for game developers. In this paper, we argue that the update strategy that is chosen by a game developer affects the number of urgent updates that are released. Although the choice of update strategy does not appear to have an impact on the percentage of updates that are released faster than the regular cycle or self-admitted hotfixes, games that use a frequent update strategy tend to have a higher proportion of 0-day updates than games that use a traditional update strategy.},

keywords = {Computer games, Steam, Update cycle, Update strategy, Urgent updates},

pubstate = {published},

tppubtype = {article}

}