With the increasing number of customer reviews on the Web, there is a growing need for effective methods to retrieve valuable information hidden in these reviews, as sellers need to gain a deep understanding of customers’ preferences in a timely manner. With the continuous enhancement of opinion mining or sentiment analysis research, researchers have proposed many automatic mining and classification methods. However, how to choose a trusted method is a difficult problem for companies, because customer reviews (or opinions) contain a lot of uncertain information and noise. This article reports on a detailed survey of recent opinion mining literature. It also reviews how to extract text features in opinions that may contain noise or uncertainties, how to express knowledge in opinions, and how to classify them. Through this extensive study, this paper discusses open questions and recommends future research directions for building the next generation of opinion mining systems.

随着Web上客户评论数量的增加，对于有效的方法来检索隐藏在这些评论中的有价值的信息的需求日益增长，因为卖家需要及时深入地了解客户的偏好。随着观点挖掘或情感分析研究的不断增强，研究人员提出了许多自动挖掘和分类方法。但是，对于公司而言，如何选择一种受信任的方法是一个难题，因为客户评论（或意见）包含许多不确定的信息和噪音。本文报告了对最近的观点挖掘文献的详细调查。它还回顾了如何从可能包含噪音或不确定性的观点中提取文本特征，如何在观点中表达知识以及如何对其进行分类。通过这项广泛的研究，