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Extracting Product Features from Reviews Using Feature Ontology Tree Applied on LDA Topic Clusters

机译：使用在LDA主题集群上应用Feature Ontology Tree的评论中提取产品特征

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Online product reviews provide data about the users perspective on the features that were experienced by them. Product features and corresponding opinions form a major part in analyzing the online product reviews. Extracting features from a huge number of reviews is categorized into three main categories such as utilizing language rules, sequence labeling and the topic modeling. Latent Dirichlet Allocation (LDA) is one such topic model which clusters the document words into unsupervised learned topics using Dirichlet priors. The words so clustered are the features and opinion words in the product reviews domain. These clusters contain words which are non features of the product. To identify appropriate product features from these clusters a hierarchical, domain independent Feature Ontology Tree (FOT) is applied to LDA clusters. This improves the accuracy of the features using extracted LDA topic clusters.
机译：在线产品评论提供有关用户透视图的数据，请参阅它们所经历的功能。产品特征和相应的意见表明了分析在线产品评论的主要部分。从大量评论中提取功能分为三个主要类别，例如利用语言规则，序列标记和主题建模。潜在的Dirichlet分配（LDA）是一个这样的主题模型，将文档单词与使用Dirichlet Priorors群集的文档单词群集。如此群集的单词是产品评论域中的功能和意见单词。这些群集包含非产品的非特征的单词。要从这些集群中标识适当的产品功能，将域独立特征本体树（FOT）应用于LDA集群。这提高了使用提取的LDA主题集群的功能的准确性。

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