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Alternative TitleExtracting Opinion Explanations from Chinese Online Reviews
Thesis Advisor毛文吉
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword观点挖掘 观点解释 因果提取 Opinion Mining Opinion Explanation Causal Extraction
Abstract近年来,观点挖掘领域的研究工作已在实际应用中显示出了巨大的价值。已有的工作主要关注观点本身的提取,如情感倾向、观点持有者、评价对象等,从而帮助人们获得特定信息源对某一具体主题的整体情感。然而,特定情感产生的原因和可能造成的影响是多种多样的,忽略这些信息可能会给用户造成困惑,甚至误导。目前的情感挖掘工作还没有将观点的解释这一对多种应用具有潜在价值的信息作为研究的对象。 为了应对这一挑战,本文在传统观点挖掘工作的基础上,对观点产生的原因和可能造成的影响,即观点的解释进行了探索和研究,并根据在线评论中文本的特点设计算法,自动提取观点解释。 本文完成的主要工作包括: 1) 本文首先总结了已有的观点挖掘工作的主要任务和方法,并在此基础上引入了观点解释的概念。通过分析已有工作在提取内容方面的局限性,阐明了观点解释提取的必要性。 2) 设计规则,提取评论文本中包含因果提示词(Causal Indicator)的观点解释。以“词对”作为特征,提取不包含因果提示词的观点解释。同时,基于“词对”特征改进了包含因果提示词的观点解释提取。 3) 通过采用语义词典,基于词语之间的相似度,对“词对”特征进行聚类,改进对观点解释的提取效果。 4) 基于真实的宾馆评论语料库的实验,验证了本文所提出的提取因果解释方法的有效性和可行性。实验表明,我们的算法对显式和隐式观点提取准确率都取得了较好的效果。 本文工作可以改进观点挖掘对决策的支持,拓展了已有的观点挖掘研究内容。
Other AbstractRecently, Studies on opinion mining have shown great value in practical applications. Existing works mainly focus on the components of opinion, such as lexicon orientation, opinion holder and opinion targets. This works can help people understand the overall evaluation on a specific topic or object from certain source. However, the reasons and/or consequences behind an opinion can be varied. Ignorance of this valuable information may lead users’ confusion and misunderstanding. According to our review, the explanation of opinion is not taken into consideration in the current literature. To address this challenge, in this paper, we focus on the explanation of reason and/or consequence behind an opinion and propose a method to extract the explanation of opinion automatically based on characteristics of Chinese online review. The accomplishments of this work include the following aspects: a) We review the the main tasks and approaches of existing work on opinion mining and introduce the concept of opinion explanation. Through analyzing their limitations of output, we clarify the necessity of extracting the explanation of opinion. b) We design rules to extract opinion explanation with a causal indicator in Chinese online reviews. We adopt word pairs as effective features and extract opinion explanation without a causal indicator. Also we optimize the extracting results of cases with causal indicators by ranking rules with word pairs. c) We adopt thesaurus to calculate the semantic similarity of words, and improve the extraction performance when taking word pairs as features. d) We conduct experiments on a Chinese hotel review corpus to verify the effectiveness and the feasibility of our proposed method. Our algorithm for extracting explicit and implicit cases reaches a fairly good accuracy. This thesis research can help improving user experiences for decision making and expanded the output of existing opinion researches.
Other Identifier200928014628045
Document Type学位论文
Recommended Citation
GB/T 7714
李悦群. 面向汉语在线评论的观点解释提取[D]. 中国科学院自动化研究所. 中国科学院研究生院,2012.
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