Fuzzy Quantification and Opinion Mining on Qualitative Data using Feature Reduction

Dundar B., AKAY D., BORAN F. E., Ozdemir S.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, cilt.33, sa.9, ss.1840-1857, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 9
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1002/int.21917
  • Dergi Adı: INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1840-1857
  • Gazi Üniversitesi Adresli: Evet

Özet

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.