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Fuzzy Quantification and Opinion Mining on Qualitative Data using Feature Reduction
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DOI：10.1002/int.21917.png)
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Abstract
En 中文
In this paper, we propose a generic recommender system that combines opinion mining and fuzzy quantification methods for qualitative data. The proposed system has two novel aspects. First, it employs a novel semantic orientation (SO) computation method to reduce the number of extracted features and opinion expressions. By using this new SO computation method, the proposed recommender system finds out the most related features and opinion expressions. Second, the proposed system generates short summary sentences from qualitative data using fuzzy quantification. The proposed system is evaluated using a restaurant review dataset. The results present that fuzzy quantified sentences offer brief information about the restaurant features from customers' feedback. In addition, opinion mining extracts positive, negative, and neutral emotions from reviews.
Keywords:
SERVICE QUALITY
RECOMMENDER
MODEL
REPRESENTATION
DISCOVERY
RULES
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