NDLI: Domain customization for aspect-oriented opinion analysis with multi-level latent sentiment clues

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Domain customization for aspect-oriented opinion analysis with multi-level latent sentiment clues

Content Provider	ACM Digital Library
Author	Guo, Zhili Zhu, Huijia Guo, Honglei Su, Zhong
Abstract	Aspect-oriented opinion mining detects the reviewers' sentiment orientation (e.g. positive, negative or neutral) towards different product-features. Domain customization is a big challenge for opinion mining due to the accuracy loss across domains. In this paper, we show our experiences and lessons learned in the domain customization for the aspect-oriented opinion analysis system OpinionIt. We present a customization method for sentiment classification with multi-level latent sentiment clues. We first construct Latent Semantic Association model to capture latent association among product-features from the unlabeled corpus. Meanwhile, we present an unsupervised method to effectively extract various domain-specific sentiment clues from the unlabeled corpus. In the customization, we tune the sentiment classifier on the labeled source domain data by incorporating the multi-level latent sentiment clues (e.g. latent association among product-features, domain-specific and generic sentiment clues). Experimental results show that the proposed method significantly reduces the accuracy loss of sentiment classification without any labeled target domain data.
Starting Page	2493
Ending Page	2496
Page Count	4
File Format	PDF
ISBN	9781450307178
DOI	10.1145/2063576.2064000
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Domain customization Supervised learning Sentiment classification Opinion mining
Content Type	Text
Resource Type	Article

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