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텍스트 마이닝을 이용한 온라인 리뷰 데이터의 레스토랑 선택속성과 선호도에 관한 연구

A Study of Restaurant Selection Attributes and Preferences in Online Review Data Using Text Mining

박나희
관광경영학회 (Tourism Management Research Organization)|2021.07|관광경영연구 (Journal of Tourism Management Research)|제25권 제4호|pp. 41-62

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참고문헌

키워드

Text MiningBig dataSelection AttributesCustomer PreferenceRestaurant Review

초록

The purpose of this study was to extract and analyze the extensive review data of tourists accumulated on the Tripadvisor travel community website to identify the restaurant selection attributes and preference factors of foreign tourists, and to derive meaningful results. In order to identify restaurant selection attributes and important preference factors in online reviews generated by restaurant customers, data was refined through text mining techniques and text network analysis was performed to reveal the structural with restaurant selection attributes. For the texts extracted by crawling, a frequency matrix was created by the word frequency list and key words using the TEXTOM program. Also, using Netdraw programs, visualized the results of the sementic network analysis and centreality of the extracted words and the structural equivalence of the words. As a result of the analysis, food, restaurant, good, place, korean, seoul, try, service and great words were found to be the main attributes in frequency and centrality. Additionally the attributes were categorized atmosphere, value, purpose and food by CONCOR analysis. Based on these findings, I would like to present theoretical and practical implications for market segmentation and marketing strategies to the restaurant industry.

Abstract

The purpose of this study was to extract and analyze the extensive review data of tourists accumulated on the Tripadvisor travel community website to identify the restaurant selection attributes and preference factors of foreign tourists, and to derive meaningful results. In order to identify restaurant selection attributes and important preference factors in online reviews generated by restaurant customers, data was refined through text mining techniques and text network analysis was performed to reveal the structural with restaurant selection attributes. For the texts extracted by crawling, a frequency matrix was created by the word frequency list and key words using the TEXTOM program. Also, using Netdraw programs, visualized the results of the sementic network analysis and centreality of the extracted words and the structural equivalence of the words. As a result of the analysis, food, restaurant, good, place, korean, seoul, try, service and great words were found to be the main attributes in frequency and centrality. Additionally the attributes were categorized atmosphere, value, purpose and food by CONCOR analysis. Based on these findings, I would like to present theoretical and practical implications for market segmentation and marketing strategies to the restaurant industry.

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