中文摘要：

在线用户评论向消费者提供了丰富的商品信息，帮助他们挑选从日常用品到娱乐活动相关的商品.然而，评论的数量之大让用户难以对商品有一个清晰的认识.现有解决电子商务网站中评论信息过载问题的方法包括评估评论质量以及总结评论观点等.但是，基于评论质量排序的方法可能信息冗余，而评论总结方法忽视上下文导致易读性较差.因此，需要实现有效的评论选择方法.设计了基于字典和规则以及基于主题模型LDA的观点获取算法来形式化地表示每条评论；提出一种基于贪心算法的评论选择方法，实现从商品评论集中选择一组高质量的评论，并最大化评论集的商品属性覆盖度和评论观点多样性.最后在真实数据集上对算法进行实验来验证该算法,实验结果表明了该算法的有效性.

英文摘要：

Online user-generated reviews provide consumers with abundant information, which influences their shopping decisions on a variety of products from daily consumption to entertainment. Due to the sheer size of the reviews, users are prevented from a clear picture of products. In fact, it is not easy for them to go through all reviews for each item. Existing solutions to information overload in ecommerce sites include estimating the quality of reviews and summarizing the opinions from the reviews. However, review ranking based on review quality may lead to information redundancy while review summarization fails to provide the context of reviews, resulting in poor readability. To this end, the paper aims at implementing an effective review selection method. We design two opinion extraction algorithms, which are dictionary and rule-based, and LDA-based respectively, to represent each review. A greedy approach is proposed to select a small set of high quality reviews for each product, and to maximize both the attribute coverage and opinion diversity. A set of experimental results on real datasets show that the proposed method is effective, and for the two opinion extraction algorithms, the dictionary and rule-based algorithm performs better than the LDA-based algorithm in solving review selection problem.