[Objective] This paper tries to construct a new quality evaluation system for answers from a Q&A community (Zhihu in China). [Methods] First, we established a quality criteria based on user evaluation and data characteristics. Then, we created vectors for the answers. Third, we used the SVM model to learn the label representation of texts as well as the accuracy of text classification. [Results] The proposed system yielded a classification accuracy of 85.32%, which is higher than the one only included user evaluation criteria (61.44%) and the other one only adopted data characteristics (79.10%). [Limitations] Our evaluation method might be biased due to the subjective annotations. [Conclusions] The proposed method is an effective way to evaluate answer quality of the Q&A community.
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