：海量的在线评论表达了用户对于图书整体及其内容、印刷等属性的观点与态度，能够反映图书的影响力。文章对图书在线评论进行多层次细粒度挖掘从而评价图书影响力。首先获取图书的在线评论数据，然后细粒度挖掘图书评论以获取评价指标，并且通过比较多个文本表示方法及构建领域情感词典提高指标挖掘的性能，最后整合评价指标获取图书影响力结果。实验结果表明，虽然存在学科差异，但是图书的在线评论可以作为图书影响力评价的有效资源。此外，在研究语料中，基于One-hot文本表示方法的评论情感判断性能优于基于主题模型与基于深度学习的方法，同时，考虑模糊情感词的情感词典能够有效提升情感判断的性能。

Massive online reviews express users' opinions and attitudes towards books and their aspects (e.g. content, printing etc.), thus reflecting the impacts of books. This paper conducted multi-level fine-grained mining of online book reviews to evaluate the impacts of books. This paper first collected books' online reviews. Then, fine-grained book review mining was conducted to get evaluation metrics, and multiple text representation methods were compared and domain sentiment lexicons were constructed to improve the performance of metric mining. Finally, evaluation metrics were integrated to identify book impacts. The experimental results show that although there are disciplines differences, online book reviews can be used as an effective resource for book impact assessment. In addition, in this corpus, the performance of one-hot text representation is superior to that of topic model based methods and the deep learning based method. Meanwhile, sentiment lexicon with fuzzy sentiment words can effectively improve the performance of sentiment classification.