Feature selection for helpfulness prediction of online product reviews: An empirical study

Autoři: Jiahua Du aff001;  Jia Rong aff001;  Sandra Michalska aff001;  Hua Wang aff001;  Yanchun Zhang aff001
Působiště autorů: Institute of Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC, Australia aff001;  Faculty of Information Technology, Monash University, Clayton, VIC, Australia aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226902


Online product reviews underpin nearly all e-shopping activities. The high volume of data, as well as various online review quality, puts growing pressure on automated approaches for informative content prioritization. Despite a substantial body of literature on review helpfulness prediction, the rationale behind specific feature selection is largely under-studied. Also, the current works tend to concentrate on domain- and/or platform-dependent feature curation, lacking wider generalization. Moreover, the issue of result comparability and reproducibility occurs due to frequent data and source code unavailability. This study addresses the gaps through the most comprehensive feature identification, evaluation, and selection. To this end, the 30 most frequently used content-based features are first identified from 149 relevant research papers and grouped into five coherent categories. The features are then selected to perform helpfulness prediction on six domains of the largest publicly available Amazon 5-core dataset. Three scenarios for feature selection are considered: (i) individual features, (ii) features within each category, and (iii) all features. Empirical results demonstrate that semantics plays a dominant role in predicting informative reviews, followed by sentiment, and other features. Finally, feature combination patterns and selection guidelines across domains are summarized to enhance customer experience in today’s prevalent e-commerce environment. The computational framework for helpfulness prediction used in the study have been released to facilitate result comparability and reproducibility.

Klíčová slova:

Grammar – Lexicons – Linguistic morphology – Metadata – Reproducibility – Semantics – Syntax – Vocabulary


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