摘要
大量的网络评论已经成为挖掘用户意见、改进产品质量的重要信息来源,而特征抽取作为后续分析的基础,直接影响到最终意见挖掘结果的准确性.本文提出了一种PMI-Bootstrapping算法,并结合了语言规则实现中文网络评论的产品特征抽取.首先利用语言规则产生候选特征集,计算每个候选特征与初始给定种子集的加权平均互信息,将满足阈值的候选特征添加到种子集中,如此循环迭代,直到种子集合收敛,输出排队后的种子集合作为抽取结果.实验证明,该算法取得良好的准确率和召回率.
Now online reviews have become an important resource for mining users＇ opinion and refining products. As a foundation of further analysis, features extraction influences the precision of the opinion mining results. This paper proposes a PMI-Bootstrapping algorithm which realizes extracting product features from Chinese online reviews by combining three language rules. First, utilize the language rules to get a candidate feature set. Then, calculate the weighted average PMI for each candidate feature with the seeds in the initial seed set. Add the candidate feature which satisfies the threshold to the seed set. Iterate until the seed set is convergent. Output the seed set as the extraction result. Experimental results show that the algorithm achieved very good precision and recall rate.
出处
《计算机系统应用》
2014年第5期196-201,共6页
Computer Systems & Applications
