フジサワ　キミヤ   FUJISAWA, Kimiya   藤澤　公也    所属   メディア学部 メディア学科    職種   准教授
言語種別	英語
発行・発表の年月	2020/10
形態種別	学術論文
査読	査読あり
標題	Bidirectional GRU for Targeted Aspect-Based Sentiment Analysis Based on Character-Enhanced Token-Embedding and Multi-Level Attention
執筆形態	共著
掲載誌名	International Journal of Intelligent Engineering and Systems
掲載区分	国外
出版社・発行元	INASS
巻・号・頁	13(5),pp.392-407
著者・共著者	Esther Irawati Setiawan, Ferry Ferry, Joan Santoso, Surya Sumpeno, Kimiya Fujisawa, Mauridhi Hery Purnomo
概要	The user's feedback on healthcare services is usually based on ratings from post-service questionnaires. However, in order to get a clear view of the user's perspective, online text reviews need to be analyzed. We combined targeted and aspect-based sentiment analysis by multi-level attention to get a specific user sentiment on a target of an aspect. The multi-level attention consists of Target-level and Sentence-level attention. Our proposed framework is based on Bidirectional Gated Recurrent Unit. Bi-GRU is commonly known to have comparable results compared to LSTM while having lesser computational complexity. We also utilized Bidirectional LSTM based Character-Enhanced Token-Embedding to handle out of vocabulary words and misspelling to avoid error in detecting sentiment. We created a dataset of online healthcare reviews from 2018-2020, targeting the name of the hospital or department, with ten aspects: cleanliness, cost, doctor, food, nurse, parking, receptionist and billing, safety, test and examination, and waiting time. To improve the results of our proposed method, we calculated polarity weight to handle imbalanced aspects in the dataset. We classified these reviews into three polarities, which are positive, negative, and neutral. Based on our experiments, we achieved the best F1-Score of 88%.