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OpinionMiner:一种新的网络意见挖掘和提取机器学习系统

OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction
课程网址: http://videolectures.net/kdd09_srihari_omnmlswome/  
主讲教师: Rohini K Srihari
开课单位: 布法罗大学
开课时间: 2009-09-14
课程语种: 英语
中文简介:
在网上销售产品的商家经常要求他们的客户分享他们的意见并亲自体验他们购买的产品。不幸的是,阅读所有客户评论很困难,特别是对于热门项目,评论的数量可能高达数百甚至数千。这使得潜在客户难以阅读它们以做出明智的决定。在这项工作中设计的OpinionMiner系统旨在挖掘客户对产品的评论,并提取高度详细的产品实体,评论者可以在这些实体上表达他们的意见。确定意见表达,并将每个公认的产品实体的意见取向分类为正面或负面。与以前采用基于规则或统计技术的方法不同,我们提出了一种在词汇化HMM框架下构建的新型机器学习方法。该方法自然地将多个重要的语言特征集成到自动学习中。在本文中,我们描述了系统的体系结构和主要组件。基于处理来自亚马逊和其他公开数据集的在线产品评论,提出了对所提出方法的评估。
课程简介: Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. Unfortunately, reading through all customer reviews is difficult, especially for popular items, the number of reviews can be up to hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision. The OpinionMiner system designed in this work aims to mine customer reviews of a product and extract high detailed product entities on which reviewers express their opinions. Opinion expressions are identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that employed rule-based or statistical techniques, we propose a novel machine learning approach built under the framework of lexicalized HMMs. The approach naturally integrates multiple important linguistic features into automatic learning. In this paper, we describe the architecture and main components of the system. The evaluation of the proposed method is presented based on processing the online product reviews from Amazon and other publicly available datasets.
关 键 词: 产品实体; 意见取向; 词汇化HMM框架
课程来源: 视频讲座网
最后编审: 2019-05-10:lxf
阅读次数: 31