Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)
- 2020.05a
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- Pages.389-392
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- 2020
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
Rating Individual Food Items of Restaurant Menu based on Online Customer Reviews using Text Mining Technique
신뢰성있는 온라인 고객 리뷰 텍스트 마이닝 기반 식당 개별 음식 아이템 평가
- Syed, Muzamil Hussain (Dept. of Information and Telecommunication, Graduate School, Soongsil University) ;
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Chung, Sun-Tae
(Dept. of Smart System Software, Soongsil University)
- 무자밀 후세인 사이드 (숭실대학교 정보통신공학과) ;
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정선태
(숭실대학교 스마트시스템소프트웨어학과)
- Published : 2020.05.29
Abstract
The growth in social media, blogs and restaurant listing directories have led to increasing customer reviews about restaurants, their quality of food items and services available on the internet. These user reviews offer a massive amount of valuable information that can be used for various decision-making purposes. Currently, most food recommendation sites provide recommendation scores about restaurants rather than food items of the restaurant and the provided recommendation scores may be biased since they are calculated only from user reviews listed only in their sites. Usually, people wants a reliable recommendation about foods, not restaurant. In this paper, we present a reliable Korean food items rating method; we first extract food items by applying NER technique to restaurant reviews collected from many Korean restaurant recommendation web sites, blogs and web data. Then, we apply lexicon-based sentiment analysis on collected user reviews and predict people's opinions as sentiment polarity scores (+1 for positive; -1 for negative; 0 for neutral). Finally, by taking average of all calculated polarity scores about a food item, we obtain a rating to individual menu items of the restaurant. The proposed food item rating is more reliable since it does not depend on reviews of only one site.
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