[Objective] We propose a model to identify useful online Chinese reviews, which helps consumers make purchasing decisions. [Methods] First, we calculated six attributes affecting the usefulness of online reviews based on their form and content characteristics. Then, we constructed a usefulness evaluation system with the weighted grey relational degree analysis method. Finally, we created a model to retrieve useful online reviews with k-means clustering method. [Results] We examined the effectiveness of our model with online reviews from Amazon.com. The recall, precision and F values showed that our method could effectively identify the useful online reviews, and classify the polarity ones. [Limitations] The samples, metrics and e-commerce platforms could be further improved. [Conclusions] The proposed method could rank and classify online reviews accurately and reliably.
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