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JKM > Volume 43(1); 2022 > Article
Kim, Jo, and Kim: Reviews Analysis of Korean Clinics Using LDA Topic Modeling

Abstract

Objectives

In the health care industry, the influence of online reviews is growing. As medical services are provided mainly by providers, those services have been managed by hospitals and clinics. However, direct promotions of medical services by providers are legally forbidden. Due to this reason, consumers, like patients and clients, search a lot of reviews on the Internet to get any information about hospitals, treatments, prices, etc. It can be determined that online reviews indicate the quality of hospitals, and that analysis should be done for sustainable hospital marketing.

Method

Using a Python-based crawler, we collected reviews, written by real patients, who had experienced Korean medicine, about more than 14,000 reviews. To extract the most representative words, reviews were divided by positive and negative; after that reviews were pre-processed to get only nouns and adjectives to get TF(Term Frequency), DF(Document Frequency), and TF-IDF(Term Frequency – Inverse Document Frequency). Finally, to get some topics about reviews, aggregations of extracted words were analyzed by using LDA(Latent Dirichlet Allocation) methods. To avoid overlap, the number of topics is set by Davis visualization.

Results and Conclusions

6 and 3 topics extracted in each positive/negative review, analyzed by LDA Topic Model. The main factors, consisting of topics were 1) Response to patients and customers. 2) Customized treatment (consultation) and management. 3) Hospital/Clinic’s environments.

Fig. 1
Distribution and Most Relevant Terms for positive Topic 1 (Topic number (ex.Topic ‘5’) are ignored)
jkm-43-1-73f1.gif
Fig. 2
Distribution and Most Relevant Terms for positive Topic 2 (Topic number (ex.Topic ‘6’) are ignored)
jkm-43-1-73f2.gif
Fig. 3
Distribution and Most Relevant Terms for positive Topic 3 (Topic number (ex.Topic ‘1’) are ignored)
jkm-43-1-73f3.gif
Fig. 4
Distribution and Most Relevant Terms for positive Topic 4 (Topic number (ex.Topic ‘2’) are ignored)
jkm-43-1-73f4.gif
Fig. 5
Distribution and Most Relevant Terms for positive Topic 5 (Topic number (ex.Topic ‘3’) are ignored)
jkm-43-1-73f5.gif
Fig. 6
Distribution and Most Relevant Terms for positive Topic 6 (Topic number (ex.Topic ‘4’) are ignored)
jkm-43-1-73f6.gif
Fig. 7
Distribution and Most Relevant Terms for positive Topic 1 (Topic number (ex.Topic ‘3’) are ignored)
jkm-43-1-73f7.gif
Fig. 8
Distribution and Most Relevant Terms for positive Topic 2 (Topic number (ex.Topic ‘3’) are ignored)
jkm-43-1-73f8.gif
Fig. 9
Distribution and Most Relevant Terms for positive Topic 3 (Topic number (ex.Topic ‘2’) are ignored)
jkm-43-1-73f9.gif
Table 1
The number of hospitals and positive/negative reviews by region
지역 서울 경기 인천 대구 대전 광주 울산 경남 전북
병원수 1369 978 246 256 164 164 199 132 95
긍정리뷰 수 5644 3482 973 669 595 496 484 408 347
부정리뷰 수 244 114 27 15 16 10 16 10 11

지역 경북 부산 충남 충북 강원 전남 세종 제주

병원수 93 82 76 75 61 61 22 6
긍정리뷰 수 271 259 255 206 193 141 72 10
부정리뷰수 10 6 9 5 3 7 1 0
Table 2
Positive review topics and extracted representative keywords by using LDA model
word 1 word 2 word 3 word 4 word 5 word 6 word 7 word 8 word 9 word 10 word 11 word 12 word 13 word 14 word 16
Topic 1 보약 전문 한결 손님 양방 산후 손가락 오다 엄마 골반 어머니 대화 염좌 강추 위주
Topic 2 감기 흉터 결제 기구 상가 기운 피로 질병 멀리 기대 안정 면역 생기 아파트 요새
Topic 3 치료 진료 병원 선생님 방문 원장 한의원 설명 직원 의사 효과 추천 물리치료 정말 의원
Topic 4 치료 방문 진료 허리 선생님 통증 물리치료 병원 어깨 원장 설명 추나 의사 한의원 직원
Topic 5 체질 한약 다이어트 검사 진맥 확인 처방 상담 복용 곳도 중간 효과 음식 방문 통계
Topic 6 입원 리뷰 오픈 컨디션 무척 인대 처리 나니 염증 교통사고 이름 수술 개원 보험 나은
Table 3
Negative review topics and extracted representative keywords by using LDA model
Word 1 word 2 word 3 word 4 word 5 word 6 word 7 word 8 word 9 word 10 word 11 word 12 word 13 word 14 word 16
Topic 1 치료 진료 병원 방문 물리치료 의사 효과 선생님 한의원 원장 허리 간호사 통증 의원 설명
Topic 2 지인 체질 진단 상담 한약 추천 후기 보험 건강 소개 한의원 확인 주차 초진 보통
Topic 3 진료 환자 보고 다이어트 설명 직원 효과 상담 병원 처방 원장 검사 선생님 대기 느낌

참고문헌

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