周清清 章成志

摘   要：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。

关键词：图书影响力评价;在线评论;评论挖掘;文本表示;情感词典

Abstract 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.

Key words book impact assessment; online reviews; review mining; text representation; sentiment lexicon

圖书影响力评价研究能够推荐优秀作品，为大众读者提供信息参考，为科研人员提供信息指导，帮助了解学科发展趋势与前沿方向。同时能够指导科研成果的评定，帮助识别高影响力作者，并为评价科研机构的学术竞争力提供依据。因此，判断图书的影响力是非常有意义的。现有研究多数利用图书的被引频次评价图书影响力[1-2]。此外，大量的替代计量资源也被广泛用于评价图书的影响力，如馆藏信息[3]、借阅信息[4]等。然而，被引频次与替代计量指标均忽略了内容层面的信息，无法识别用户的真实意图。

与此同时，随着Web 2.0的迅速发展，人们越来越倾向于在线购买图书，并留下了海量的在线图书评论。……