ホウチン　テルヒサ   Teruhisa Hochin   寶珍　輝尚    所属   追手門学院大学  理工学部 情報工学科    職種   教授
言語種別	英語
発行・発表の年月	2020/09
形態種別	論文
査読	査読あり
標題	Rating Estimation from Review Texts Using Long Short-Term Memory
執筆形態	共著・編著（代表編著を除く）
掲載誌名	Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
掲載区分	国外
出版社・発行元	IEEE
巻・号・頁	pp.671-676
著者・共著者	Ryo Takada,Teruhisa Hochin,Hiroki Nomiya
概要	A lot of reviews of products have been posted on various web sites and services because of the spread of the Internet, and the estimation of ratings from review texts is actively performed. However, there are few such studies on Japanese review texts without limiting the product genre. In this paper, we propose a neural network model that takes as input a general Japanese product review text and estimates rating for it without limiting the product genre. By using Long Short-Term Memory (LSTM), which is one of the regression type neural network models that can handle sequential data, we analyze words in sentences considering their order. The rating estimation model is realized mainly by segmentation of texts, conversion to distributed representations, an LSTM layer, and a fully connected layer. In addition, we conduct evaluation experiments of the created model and consider the results.
DOI	10.1109/IIAI-AAI50415.2020.00011
DBLP ID	conf/iiaiaai/TakadaHN20
PermalinkURL	https://dblp.uni-trier.de/rec/conf/iiaiaai/2020
researchmap用URL	https://dblp.uni-trier.de/db/conf/iiaiaai/iiaiaai2020.html#TakadaHN20