文章基本信息

标题：FEROM: Feature Extraction and Refinement for Opinion Mining
本地全文：下载
作者：Jeong, Ha-Na ; Shin, Dong-Wook ; Choi, Joong-Min 等
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2011
卷号：33
期号：5
页码：720-730
DOI：10.4218/etrij.11.0110.0627
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.
关键词：Customer review analysis;feature-based opinion mining;feature extraction;feature refinement