Sentiment analysis of online reviews is an important way of mining useful information from the Internet. Despite several advantages, the accuracy of sentiment analysis based on a domain dictionary relies on the comprehensiveness and accuracy of the dictionary. Instead of creating a domain dictionary, we propose an approach for online review sentiment classification, which uses a conditional random field algorithm to extract the emotional characteristics from fragments of the review. The characteristic (feature) words are then weighted asymmetrically before a support vector machine classifier is used to obtain the sentiment orientation of the review. In our experiments, the average accuracy reached 90%, showing that using sentiment feature fragments instead of whole reviews and weighting the characteristic words asymmetrically can improve the sentiment classification accuracy.

中文翻译：

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使用条件随机字段和支持向量机进行在线评论的情感分析

在线评论的情感分析是从Internet挖掘有用信息的一种重要方法。尽管有几个优点，但基于域词典的情感分析的准确性取决于词典的全面性和准确性。代替创建域词典，我们提出了一种在线评论情感分类的方法，该方法使用条件随机场算法从评论片段中提取情感特征。然后，在使用支持向量机分类器获取评论的情感方向之前，对特征（特征）词进行非对称加权。在我们的实验中，平均准确度达到90％，