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LDA를 이용한 온라인 리뷰의 다중 토픽별 감성분석 - TripAdvisor 사례를 중심으로 -
홍태호 ( Hong Tae-ho ),니우한잉 ( Niu Hanying ),임강 ( Ren Gang ),박지영 ( Park Ji-young ) 한국정보시스템학회 2018 情報시스템硏究 Vol.27 No.1
Purpose There is much information in customer reviews, but finding key information in many texts is not easy. Business decision makers need a model to solve this problem. In this study we propose a multi-topic sentiment analysis approach using Latent Dirichlet Allocation (LDA) for user-generated contents (UGC). Design/methodology/approach In this paper, we collected a total of 104,039 hotel reviews in seven of the world's top tourist destinations from TripAdvisor (www.tripadvisor.com) and extracted 30 topics related to the hotel from all customer reviews using the LDA model. Six major dimensions (value, cleanliness, rooms, service, location, and sleep quality) were selected from the 30 extracted topics. To analyze data, we employed R language. Findings This study contributes to propose a lexicon-based sentiment analysis approach for the keywords-embedded sentences related to the six dimensions within a review. The performance of the proposed model was evaluated by comparing the sentiment analysis results of each topic with the real attribute ratings provided by the platform. The results show its outperformance, with a high ratio of accuracy and recall. Through our proposed model, it is expected to analyze the customers’ sentiments over different topics for those reviews with an absence of the detailed attribute ratings.
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