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一种新的基于词汇化HMM的Web意见挖掘学习框架
A Novel Lexicalized HMM-Based Learning Framework for Web Opinion Mining |
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| 课程网址: |
http://videolectures.net/icml09_jin_nlhmmblfwom/
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| 主讲教师: |
Wei Jin
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| 开课单位: |
北达科他州立大学
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| 开课时间: |
2009-08-26
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| 课程语种: |
英语
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| 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。随着电子商务变得越来越流行，产品接收的客户评论数量迅速增长。这使得潜在客户难以阅读它们以便就是否购买产品做出明智的决定。在这项研究中，我们的目标是挖掘产品的客户评论，并提取高度具体的产品相关实体，评论者在这些实体上表达他们的意见。意见表达和句子也被识别，每个公认的产品实体的意见取向被分类为正面或负面。与以前主要依赖于自然语言处理技术或统计信息的方法不同，我们提出了一种使用词汇化HMM的新型机器学习框架。该方法自然地将语言特征（例如词性和周围的语境线索）集成到自动学习中。实验结果证明了所提方法在网络意见挖掘和产品评论中的有效性。
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| 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. As e-commerce is becoming more and more popular, the number of customer reviews a product receives grows rapidly. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. In this research, we aim to mine customer reviews of a product and extract highly specific product related entities on which reviewers express their opinions. Opinion expressions and sentences are also identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that have mostly relied on natural language processing techniques or statistic information, we propose a novel machine learning framework using lexicalized HMMs. The approach naturally integrates linguistic features, such as part-of-speech and surrounding contextual clues of words into automatic learning. The experimental results demonstrate the effectiveness of the proposed approach in web opinion mining and extraction from product reviews.
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| 关 键 词: |
电子商务; 词汇化; 语言特征
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-23：lxf
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| 阅读次数: |
96
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