{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:57:48Z","timestamp":1760597868116},"reference-count":6,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11n12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2018,11]]},"abstract":"<jats:p>In the field of target-based sentiment analysis, the deep neural model combining attention mechanism is a remarkable success. In current research, it is commonly seen that attention mechanism is combined with Long Short-Term Memory (LSTM) networks. However, such neural network-based architectures generally rely on complex computation and only focus on single target. In this paper, we propose a gated hierarchical LSTM (GH-LSTMs) model which combines regional LSTM and sentence-level LSTM via a gated operation for the task of target-based sentiment analysis. This approach can distinguish different polarities of sentiment of different targets in the same sentence through a regional LSTM. Furthermore, it is able to concentrate on the long-distance dependency of target in the whole sentence via a sentence-level LSTM. The final results of our experiments on multi-domain datasets of two languages from SemEval 2016 indicate that our approach yields better performance than Support Vector Machine (SVM) and several typical neural network models. A case study of some typical examples also makes a supplement to this conclusion.<\/jats:p>","DOI":"10.1142\/s0218194018400259","type":"journal-article","created":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T08:44:18Z","timestamp":1547541858000},"page":"1719-1737","source":"Crossref","is-referenced-by-count":5,"title":["Gated Hierarchical LSTMs for Target-Based Sentiment Analysis"],"prefix":"10.1142","volume":"28","author":[{"given":"Hao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baowen","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,1,15]]},"reference":[{"key":"S0218194018400259BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2014.04.011"},{"key":"S0218194018400259BIB003","doi-asserted-by":"publisher","DOI":"10.1561\/1500000011"},{"key":"S0218194018400259BIB005","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-008-9070-z"},{"key":"S0218194018400259BIB009","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00097"},{"key":"S0218194018400259BIB013","series-title":"Synthesis Lectures on Human Language Technologies","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-02145-9","volume-title":"Sentiment Analysis and Opinion Mining","volume":"5","author":"Liu B.","year":"2012"},{"key":"S0218194018400259BIB020","doi-asserted-by":"publisher","DOI":"10.1162\/coli_a_00034"}],"container-title":["International Journal of Software Engineering and Knowledge Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218194018400259","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T07:26:49Z","timestamp":1662794809000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218194018400259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11]]},"references-count":6,"journal-issue":{"issue":"11n12","published-online":{"date-parts":[[2019,1,15]]},"published-print":{"date-parts":[[2018,11]]}},"alternative-id":["10.1142\/S0218194018400259"],"URL":"https:\/\/doi.org\/10.1142\/s0218194018400259","relation":{},"ISSN":["0218-1940","1793-6403"],"issn-type":[{"value":"0218-1940","type":"print"},{"value":"1793-6403","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11]]}}}