Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification
Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification
复制标题
将评价表达模式纳入主题建模以进行方面和情感词识别
DOI：
10.1016/j.knosys.2014.02.003
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发表时间：
2014-05
影响因子：
8.8
通讯作者：
Song Meina
中科院分区：
文献类型：
--
作者：
Zheng Xiaolin;Lin Zhen;Wang Xiaowei;Lin Kwei-Jay;Song Meina
随着用户生成内容的大幅增长，在线评论正在成为挖掘客户对产品和服务的意见的极有价值的来源。然而，大多数传统的意见挖掘方法是粗粒度的，不能理解自然语言。因此，基于方面的意见挖掘和摘要是学术界和工业界的研究热点。在本文中，我们研究了一种方法来提取产品和服务方面的话，以及情感词，自动从评论。提出了一种基于无监督依赖分析的评价表达模式提取方法。评价表达模式代表了人们对产品或服务表达意见的方式，可以看作是体貌词和情感词之间句法关系的浓缩表示。AEP是高级的、独立于领域的信息类型，具有良好的领域适应性。提出了一种基于AEP的潜在狄利克雷分配模型（AEP-LDA）。这是一个概率生成模型，它假设一个句子中的所有单词都来自一个主题--根据我们的观察，这是一个普遍正确的假设。该模型还假设每个评论语料库是由几个相互对应的方面和情感主题，以及一个背景词主题。AEP信息被纳入AEP-LDA模型，同时挖掘方面和情感词。对餐馆、酒店、MP3播放器和摄像头的评论的实验结果表明，AEP-LDA模型在识别体词和情感词方面优于其他方法。
With the considerable growth of user-generated content, online reviews are becoming extremely valuable sources for mining customers’ opinions on products and services. However, most of the traditional opinion mining methods are coarse-grained and cannot understand natural languages. Thus, aspect-based opinion mining and summarization are of great interest in academic and industrial research. In this paper, we study an approach to extract product and service aspect words, as well as sentiment words, automatically from reviews. An unsupervised dependency analysis-based approach is presented to extract Appraisal Expression Patterns (AEPs) from reviews, which represent the manner in which people express opinions regarding products or services and can be regarded as a condensed representation of the syntactic relationship between aspect and sentiment words. AEPs are high-level, domain-independent types of information, and have excellent domain adaptability. An AEP-based Latent Dirichlet Allocation (AEP-LDA) model is also proposed. This is a sentence-level, probabilistic generative model which assumes that all words in a sentence are drawn from one topic – a generally true assumption, based on our observation. The model also assumes that every review corpus is composed of several mutually corresponding aspect and sentiment topics, as well as a background word topic. The AEP information is incorporated into the AEP-LDA model for mining aspect and sentiment words simultaneously. The experimental results on reviews of restaurants, hotels, MP3 players, and cameras show that the AEP-LDA model outperforms other approaches in identifying aspect and sentiment words.
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DOI：
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发表时间：
2008
期刊：
--
影响因子：
--
作者：
Sasha Blair-Goldensohn;K. Hannan;Ryan T. McDonald;T. Neylon;George A. Reis;Jeffrey C. Reynar
通讯作者：
Sasha Blair-Goldensohn;K. Hannan;Ryan T. McDonald;T. Neylon;George A. Reis;Jeffrey C. Reynar
DOI：
--
发表时间：
2010-05
期刊：
--
影响因子：
--
作者：
Stefano Baccianella;Andrea Esuli;F. Sebastiani
通讯作者：
Stefano Baccianella;Andrea Esuli;F. Sebastiani
DOI：
10.1145/2396761.2396863
发表时间：
2012-10
期刊：
Proceedings of the 21st ACM international conference on Information and knowledge management
影响因子：
--
作者：
Samaneh Moghaddam;M. Ester
通讯作者：
Samaneh Moghaddam;M. Ester
DOI：
10.1073/pnas.0307752101
发表时间：
2004-04-06
影响因子：
11.1
作者：
Griffiths, TL;Steyvers, M
通讯作者：
Steyvers, M
DOI：
10.7766/orbit.v1.2.44
发表时间：
2001-01
期刊：
J. Mach. Learn. Res.
影响因子：
--
作者：
D. Blei;Andrew Y. Ng;Michael I. Jordan
通讯作者：
D. Blei;Andrew Y. Ng;Michael I. Jordan