[Objective] This paper aims to effectively extract multi-dimensional characteristics of online reviews and then examine the impact of text content to the review quality evaluation. [Methods] First, we quantified and extracted content features based on the textual and sentimental message from the reviews. Then, adopted the GBDT model to evaluate the influence of feature sets to classification results, along with greedy feature selection procedure to identify the most effective content features. Finally, we examined the influences of these features. [Results] The proposed method could improve the performance of review quality evaluation tasks, especially the recall and precision of the new system. [Limitations] Our research focused on review data from search services, and did not investigate products like movies and music. [Conclusions] The information gained from reviews and product feature words, degree of sentimental objectiveness, and differences among review contents all posed important effects to review quality evaluation.
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