Online user reviews play an important role in the assessment of product quality, and thus these reviews should be evaluated carefully. This study evaluates the helpfulness of game reviews on the online Steam store. It collects a large set of user reviews of different game genres and builds a classification model to predict whether these reviews are helpful or not. This model can accurately predict the helpfulness of the reviews based on different thresholds. This work also investigates various types of textual and word embedding features and analyzed their importance for predictions. Furthermore, it develops a regression-based model that can predict the score or rating of game reviews on Steam.
VordererP., HartmannT. and KlimmtC., Explaining the Enjoyment of Playing Video Games: The Role of Competition, In Proceedings of the Second International Conference on Entertainment Computing, ICEC ’03, Pittsburgh, Pennsylvania, USA, 2003, pp. 1–9.
2.
RogersE.M., LarsenJ.K., Silicon Valley Fever: Growth of High-Technology Culture, Allen & Unwin, 1985.
ZagalJ.P., LaddA. and JohnsonT., Characterizing and Understanding Game Reviews. In Proceedings of the 4th International Conference on Foundations of Digital Games, FDG ’09, Orlando, Florida, 2009, pp. 215–222 doi: 10.1145/1536513.1536553
7.
BarbosaJ.L.N., MouraR.S., deR.L. and SantosS., Predicting Portuguese Steam Review Helpfulness Using Artificial Neural Networks. In Proceedings of the 22Nd Brazilian Symposium on Multimedia and the Web, Webmedia ’16, Teresina, Piauí State, Brazil, 2016, pp. 287–293, doi: 10.1145/2976796.2976871
8.
KimS.-M., PantelP., ChklovskiT. and PennacchiottiM., Automatically Assessing Review Helpfulness, In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP ’06, Sydney, Australia, 2006, pp, 423–430.
9.
LiuY., HuangX. and AnA., Modeling and Predicting the Helpfulness of Online Reviews, In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 2008, pp. 443–452. doi: 10.1109/ICDM.2008.94
10.
SiersdorferS., ChelaruS., NejdlW. and San PedroJ., How Useful Are Your Comments? Analyzing and Predicting Youtube Comments and Comment Ratings, In Proceedings of the 19th International Conference on World Wide Web, WWW ’10, North Carolina, USA, 2010, pp. 891–900. ACM. doi: 10.1145/1772690.1772781
11.
YangY., YanY., QiuM. and BaoF.S., Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews, In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015, pp. 38–44. doi: 10.3115/v1/p15-2007
12.
ChenJ., ZhangC. and NiuZ., Identifying Helpful Online Reviews with Word Embedding Features, In Knowledge Science, Engineering and Management KSEM 2016 Lecture Notes in Computer Science, Springer, Cham, 2016, pp. 123–133. doi: 10.1007/978-3-319-47650-6_10
13.
Liu (Jingjing)J.J.,
CaoY.,
LinC.-Y.,
HuangY. and
ZhouM., Low-Quality Product Review Detection in Opinion Summarization. In EMNLP 2007, 2007.
14.
XieS., WangG., LinS. and YuP.S., Review Spam Detection via Temporal Pattern Discovery, In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, Beijing, China, 2012, pp. 823–831. doi: 10.1145/2339530.2339662
15.
KrishnamoorthyS., Linguistic features for review helpfulness prediction, Expert Systems with Applications42(7) (2015), 3751–3759. doi: 10.1016/j.eswa.2014.12.044
16.
TausczikY.R. and PennebakerJ.W., The psychological meaning of words: LIWC and computerized text analysis methods, Journal of Language and Social Psychology29(1) (2010), 24–54. doi: 10.1177/0261927X09351676
17.
BleiD.M., NgA.Y. and JordanM.I., Latent dirichlet allocation, Journal of Machine Learning Research (2003), 993–1022.
18.
PennebakerJ.W., BoydR.L., JordanK. and BlackburnK., The Development and Psychometric Properties of LIWC2015. http://hdl.handle.net/2152/31333, 2015.
19.
SaltonG. and McGillM.J., Introduction to Modern Information Retrieval. McGraw-Hill, 1986.
MikolovT., ChenK., CorradoG. and DeanJ., Efficient Estimation of Word Representations in Vector Space. CoRR, abs/1301.3781, 2013.
22.
MikolovT., SutskeverI., ChenK., CorradoG. and DeanJeffrey, Distributed Representations of Words and Phrases and their Compositionality. CoRR, abs/1310.4546, 2013.
JamesG., WittenD., HastieT. and TibshiraniR., An Introduction to Statistical Learning. Springer, 2013.
25.
BerminghamM.L., Pong-WongR., SpiliopoulouA., HaywardC., RudanI., CampbellH., WrightA.F., WilsonJ.F., AgakovF., NavarroP. and HaleyC.S., Application of highdimensionalfeature selection: evaluation for genomic prediction in man, Scientific Reports5(10312), 2015. doi: 10.1038/srep10312
26.
GuyonI., WestonJ., BarnhillS. and VapnikV., Gene selection for cancer classification using support vector machines, Machine Learning46(1) (2002), 389–422. doi: 10.1023/A:1012487302797
27.
GranittoP.M., FurlanelloC., BiasioliF. and GasperiF., Recursive feature elimination with random forest for ptr-ms analysis of agroindustrial products, Chemometrics and Intelligent Laboratory Systems83(2) (2006), 83–90. doi: https://doi.org/10.1016/j.chemolab.2006.01.007.
28.
FriedmanJ.H., Greedy function approximation: A gradient boosting machine, Annals of Statistics29(5) (2001), 1189–1232. doi: 10.1214/aos/1013203451
29.
KotsiantisS., KanellopoulosD. and PintelasP., Handling imbalanced datasets: A review, GESTS International Transactions on Computer Science and Engineering30 (2006).
30.
LemnaruC. and PotoleaR., Imbalanced Classification Problems: Systematic Study, Issues and Best Practices, In Enterprise Information Systems ICEIS 2011 Lecture Notesin Business Information Processing, Springer, Berlin, Heidelberg, 2012, pp. 35–50. doi: 10.1007/978-3-642-29958-2_3
31.
BreimanL., Classification and regression trees. Routledge, 2017.
32.
FriedmanJ.H. and MeulmanJ.J., Multiple additive regression trees with application in epidemiology, Statistics in Medicine22(9) (2003), 1365–1381. doi: 10.1002/sim.1501
