文章基本信息

标题：An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews
本地全文：下载
作者：Liu, Chen ; Tang, Li ; Shan, Wei 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2018
卷号：10
期号：5
页码：1-15
出版社：MDPI, Open Access Journal
摘要：How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the “bad”, “good”, “poor”, “pretty good”, “not bad” these general collocation words.
关键词：opinion mining; feature extraction; bipartite network; extended HITS algorithm