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Fuzzy Quantification and Opinion Mining on Qualitative Data using Feature Reduction
Author(s) -
Dündar Betül,
Akay Diyar,
Boran Fatih Emre,
Özdemir Suat
Publication year - 2018
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21917
Subject(s) - computer science , sentiment analysis , recommender system , data mining , fuzzy logic , computation , artificial intelligence , feature (linguistics) , machine learning , information retrieval , algorithm , linguistics , philosophy
Abstract 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.