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• W2170534931 abstract "Identifying product features from reviews is the fundamental step as well as a bottleneck in feature-level sentiment analysis. This study proposes a method of unsupervised product feature extraction for feature-oriented opinion determination. The domain-specific features are extracted by measuring the similarity distance of domain vectors. A domain vector is derived based on the association values between a feature and comparative domain corpora. A novel term similarity measure (PMI–TFIDF) is introduced to evaluate the association of candidate features and domain entities. The results show that our approach of feature extraction outperforms other state-of-the-art methods, and the only external resources used are comparative domain corpora. Therefore, it is generic and unsupervised. Compared with traditional pointwise mutual information (PMI), PMI–TFIDF showed better distinction ability. We also propose feature-oriented opinion determination based on feature-opinion pair extraction and feature-oriented opinion lexicon generation. The results demonstrate the effectiveness of our proposed method and indicate that feature-oriented opinion lexicons are superior to general opinion lexicons for feature-oriented opinion determination." @default.
• W2170534931 created "2016-06-24" @default.
• W2170534931 creator A5071943346 @default.
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• W2170534931 date "2014-07-01" @default.
• W2170534931 modified "2023-10-06" @default.
• W2170534931 title "Unsupervised product feature extraction for feature-oriented opinion determination" @default.
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• W2170534931 doi "https://doi.org/10.1016/j.ins.2014.02.063" @default.
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