On the design of LDA models for aspect-based opinion mining
On the design of LDA models for aspect-based opinion mining
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DOI：
10.1145/2396761.2396863
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发表时间：
2012-10
期刊：
影响因子：
--
通讯作者：
Samaneh Moghaddam;M. Ester
中科院分区：
文献类型：
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作者：
Samaneh Moghaddam;M. Ester
基于方面的意见挖掘旨在从客户评论中提取方面及其对应的评分，为客户做出购买决策提供非常有用的信息。在过去的几年里，已经提出了几种概率图模型来解决这个问题，其中大多数都是基于潜在狄利克雷分配(LDA)的。虽然这些模型有很多共同之处，但也有一些特征将它们区分开来。这些根本的差异与在LDA模型的设计中做出的主要决定相对应。虽然研究论文通常声称，新模式的表现优于现有模式，但通常不存在“一刀切”的模式。在本文中，我们通过讨论一系列日益复杂的LDA模型，提出了一套基于方面的意见挖掘的设计指南。我们认为，这些模型代表了已发表的主要方法的精髓，并允许我们区分各种设计决策的影响。我们在Epinions.com(500K评论)的一个非常大的真实数据集上进行了广泛的实验，并比较了不同模型在坚持测试集的可能性以及特征识别和评级预测的准确性方面的性能。
Aspect-based opinion mining, which aims to extract aspects and their corresponding ratings from customers reviews, provides very useful information for customers to make purchase decisions. In the past few years several probabilistic graphical models have been proposed to address this problem, most of them based on Latent Dirichlet Allocation (LDA). While these models have a lot in common, there are some characteristics that distinguish them from each other. These fundamental differences correspond to major decisions that have been made in the design of the LDA models. While research papers typically claim that a new model outperforms the existing ones, there is normally no "one-size-fits-all" model. In this paper, we present a set of design guidelines for aspect-based opinion mining by discussing a series of increasingly sophisticated LDA models. We argue that these models represent the essence of the major published methods and allow us to distinguish the impact of various design decisions. We conduct extensive experiments on a very large real life dataset from Epinions.com (500K reviews) and compare the performance of different models in terms of the likelihood of the held-out test set and in terms of the accuracy of aspect identification and rating prediction.