Affiliations: [a] Social Networks and Human-Centered Computing, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica and Department of Computer Science, National Chengchi University, Taipei, Taiwan
| [b]
Institute of Information Science, Academia Sinica, Taipei, Taiwan
Correspondence:
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Corresponding author. Mrinal Kanti Baowaly, Institute of Information Science, Academia Sinica. Address: No. 128, Academia Road, Section 2, Nangang District, Taipei 115, Taiwan. E-mail: [email protected] and [email protected].
Abstract: 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.
Keywords: Steam, online review, review helpfulness, semantic analysis, word embedding
