{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:11:55Z","timestamp":1775769115467,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and ICT (KR)","award":["2020-0-00368"],"award-info":[{"award-number":["2020-0-00368"]}]},{"name":"Ministry of Science and ICT (KR)","award":["2013-0-00131"],"award-info":[{"award-number":["2013-0-00131"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that support important evidences for sentiment analysis and sentences that do not, they have treated the document as a bag of sentences. In other words, they have not considered the importance of each sentence in the document. To effectively determine polarity of a document, each sentence in the document should be dealt with different degrees of importance. To address this problem, we propose a document-level sentence classification model based on deep neural networks, in which the importance degrees of sentences in documents are automatically determined through gate mechanisms. To verify our new sentiment analysis model, we conducted experiments using the sentiment datasets in the four different domains such as movie reviews, hotel reviews, restaurant reviews, and music reviews. In the experiments, the proposed model outperformed previous state-of-the-art models that do not consider importance differences of sentences in a document. The experimental results show that the importance of sentences should be considered in a document-level sentiment classification task.<\/jats:p>","DOI":"10.3390\/e22121336","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T08:59:15Z","timestamp":1606294755000},"page":"1336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Improving Document-Level Sentiment Classification Using Importance of Sentences"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6108-6755","authenticated-orcid":false,"given":"Gihyeon","family":"Choi","sequence":"first","affiliation":[{"name":"Program of Computer and Communications Engineering, College of IT, Kangwon National University, Chuncheon-si 24341, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shinhyeok","family":"Oh","sequence":"additional","affiliation":[{"name":"Program of Computer and Communications Engineering, College of IT, Kangwon National University, Chuncheon-si 24341, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8286-7198","authenticated-orcid":false,"given":"Harksoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Engineering &amp; Department of Artificial Intelligence, College of Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"ref_1","unstructured":"Baccianella, S., Esuli, A., and Sebastiani, F. 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