{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:18:08Z","timestamp":1774315088919,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s10115-022-01688-3","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T21:07:23Z","timestamp":1654031243000},"page":"1845-1861","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Aspect-based sentiment analysis with enhanced aspect-sensitive word embeddings"],"prefix":"10.1007","volume":"64","author":[{"given":"Yusi","family":"Qi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4430-5036","authenticated-orcid":false,"given":"Xiaoqing","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Xuanjing","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"1688_CR1","doi-asserted-by":"crossref","unstructured":"Bao L, Lambert P, Badia T (2019) Attention and lexicon regularized LSTM for aspect-based sentiment analysis. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, pp. 253\u2013259","DOI":"10.18653\/v1\/P19-2035"},{"key":"1688_CR2","doi-asserted-by":"crossref","unstructured":"Baroni M, Dinu G, Kruszewski G (2014) Don\u2019t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, volume 1: long papers, pp 238\u2013247","DOI":"10.3115\/v1\/P14-1023"},{"issue":"2","key":"1688_CR3","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MIS.2013.30","volume":"28","author":"E Cambria","year":"2013","unstructured":"Cambria E, Schuller B, Xia Y et al (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15\u201321","journal-title":"IEEE Intell Syst"},{"key":"1688_CR4","doi-asserted-by":"crossref","unstructured":"Chen P, Sun Z, Bing L, et al (2017) Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 452\u2013461","DOI":"10.18653\/v1\/D17-1047"},{"key":"1688_CR5","doi-asserted-by":"crossref","unstructured":"Chen Z, Qian T (2019) Transfer capsule network for aspect level sentiment classification. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 547\u2013556","DOI":"10.18653\/v1\/P19-1052"},{"key":"1688_CR6","unstructured":"Do BT (2018) Aspect-based sentiment analysis using bitmask bidirectional long short term memory networks. In: The thirty-first international flairs conference"},{"key":"1688_CR7","doi-asserted-by":"crossref","unstructured":"Dong L, Wei F , Tan C, et al (2014) Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, volume 2: short papers, pp 49\u201354","DOI":"10.3115\/v1\/P14-2009"},{"key":"1688_CR8","doi-asserted-by":"crossref","unstructured":"Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3433\u20133442","DOI":"10.18653\/v1\/D18-1380"},{"key":"1688_CR9","unstructured":"Gu S, Zhang L, Hou Y, et al (2018) A position-aware bidirectional attention network for aspect-level sentiment analysis. In: Proceedings of the 27th international conference on computational linguistics, pp 774\u2013784"},{"key":"1688_CR10","doi-asserted-by":"crossref","unstructured":"He R, Lee WS, Ng HT et al (2018) Exploiting document knowledge for aspect-level sentiment classification. arXiv preprint arXiv:1806.04346","DOI":"10.18653\/v1\/P18-2092"},{"key":"1688_CR11","doi-asserted-by":"crossref","unstructured":"He R, Lee WS, Ng HT, et al (2019) An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. arXiv preprint arXiv:1906.06906","DOI":"10.18653\/v1\/P19-1048"},{"key":"1688_CR12","doi-asserted-by":"crossref","unstructured":"Huang B, Ou Y, Carley KM (2018) Aspect level sentiment classification with attention-over-attention neural networks. In: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, Cham, pp 197\u2013206","DOI":"10.1007\/978-3-319-93372-6_22"},{"key":"1688_CR13","unstructured":"Jiang L, Yu M, Zhou M, et al (2011) Target-dependent twitter sentiment classification. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 151\u2013160"},{"key":"1688_CR14","doi-asserted-by":"crossref","unstructured":"Kiritchenko S, Zhu X, Cherry C, et al (2014) Nrc-canada-2014: detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 437\u2013442","DOI":"10.3115\/v1\/S14-2076"},{"key":"1688_CR15","doi-asserted-by":"crossref","unstructured":"Li F, Wang S, Liu S, et al (2014) Suit: a supervised user-item based topic model for sentiment analysis. In: Twenty-eighth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v28i1.8947"},{"key":"1688_CR16","doi-asserted-by":"crossref","unstructured":"Li L, Liu Y, Zhou A (2018) Hierarchical attention based position-aware network for aspect-level sentiment analysis. In: Proceedings of the 22nd conference on computational natural language learning, pp 181\u2013189","DOI":"10.18653\/v1\/K18-1018"},{"key":"1688_CR17","doi-asserted-by":"crossref","unstructured":"Li Z, Wei Y, Zhang Y, et al (2019) Exploiting coarse-to-fine task transfer for aspect-level sentiment classification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, No 01, pp 4253\u20134260","DOI":"10.1609\/aaai.v33i01.33014253"},{"key":"1688_CR18","doi-asserted-by":"crossref","unstructured":"Ma D, Li S, Zhang X, et al (2017) Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893","DOI":"10.24963\/ijcai.2017\/568"},{"key":"1688_CR19","doi-asserted-by":"crossref","unstructured":"Majumder N, Poria S, Gelbukh A, et al (2018) IARM: inter-aspect relation modeling with memory networks in aspect-based sentiment analysis. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3402\u20133411","DOI":"10.18653\/v1\/D18-1377"},{"key":"1688_CR20","unstructured":"Mohammad SM, Kiritchenko S, Zhu X (2013) NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242"},{"key":"1688_CR21","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"1688_CR22","doi-asserted-by":"crossref","unstructured":"Pontiki M, Galanis D, Papageorgiou H, et al (2015) Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 486\u2013495","DOI":"10.18653\/v1\/S15-2082"},{"key":"1688_CR23","doi-asserted-by":"crossref","unstructured":"Pontiki M, Galanis D, Papageorgiou H, et al (2016) Semeval-2016 task 5: aspect based sentiment analysis. In: International workshop on semantic evaluation, pp 19\u201330","DOI":"10.18653\/v1\/S16-1002"},{"key":"1688_CR24","doi-asserted-by":"crossref","unstructured":"Qian Q, Tian B, Huang M, et al (2015) Learning tag embeddings and tag-specific composition functions in recursive neural network. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, volume 1: long papers, pp 1365\u20131374","DOI":"10.3115\/v1\/P15-1132"},{"key":"1688_CR25","unstructured":"Socher R, Pennington J, Huang EH, et al (2011) Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the 2011 conference on empirical methods in natural language processing, pp 151\u2013161"},{"key":"1688_CR26","doi-asserted-by":"crossref","unstructured":"Song Y, Wang J, Jiang T, et al (2019) Attentional encoder network for targeted sentiment classification. arXiv preprint arXiv:1902.09314","DOI":"10.1007\/978-3-030-30490-4_9"},{"key":"1688_CR27","unstructured":"Tang D, Qin B, Feng X et al (2015) Effective LSTMs for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100"},{"key":"1688_CR28","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900","DOI":"10.18653\/v1\/D16-1021"},{"key":"1688_CR29","doi-asserted-by":"crossref","unstructured":"Tang J, Lu Z, Su J, et al (2019) Progressive self-supervised attention learning for aspect-level sentiment analysis. arXiv preprint arXiv:1906.01213","DOI":"10.18653\/v1\/P19-1053"},{"key":"1688_CR30","doi-asserted-by":"crossref","unstructured":"Tang H, Ji D, Li C, et al (2020) Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 6578\u20136588","DOI":"10.18653\/v1\/2020.acl-main.588"},{"key":"1688_CR31","doi-asserted-by":"crossref","unstructured":"Toutanova K, Klein D, Manning CD, et al (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 human language technology conference of the North American chapter of the association for computational linguistics, pp 252\u2013259","DOI":"10.3115\/1073445.1073478"},{"key":"1688_CR32","unstructured":"Vo DT, Zhang Y (2015) Target-dependent twitter sentiment classification with rich automatic features. In: Twenty-fourth international joint conference on artificial intelligence"},{"key":"1688_CR33","doi-asserted-by":"crossref","unstructured":"Vo DT, Zhang Y (2016) Don\u2019t count, predict! an automatic approach to learning sentiment lexicons for short text. In: Proceedings of the 54th annual meeting of the association for computational linguistics, vol 2, No 2, pp 219\u2013224","DOI":"10.18653\/v1\/P16-2036"},{"key":"1688_CR34","doi-asserted-by":"crossref","unstructured":"Wagner J, Arora P, Cortes S, et al (2014) Dcu: aspect-based polarity classification for semeval task 4","DOI":"10.3115\/v1\/S14-2036"},{"key":"1688_CR35","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606\u2013615","DOI":"10.18653\/v1\/D16-1058"},{"key":"1688_CR36","doi-asserted-by":"crossref","unstructured":"Wang S, Mazumder S, Liu B, et al (2018) Target-sensitive memory networks for aspect sentiment classification. In: Proceedings of the 56th annual meeting of the association for computational linguistics, volume 1: long papers","DOI":"10.18653\/v1\/P18-1088"},{"key":"1688_CR37","doi-asserted-by":"crossref","unstructured":"Wang K, Shen W, Yang Y, et al (2020) Relational graph attention network for aspect-based sentiment analysis. arXiv preprint arXiv:2004.12362","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"1688_CR38","doi-asserted-by":"crossref","unstructured":"Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of human language technology conference and conference on empirical methods in natural language processing, pp 347\u2013354","DOI":"10.3115\/1220575.1220619"},{"key":"1688_CR39","unstructured":"Zheng S, Xia R (2018) Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention. arXiv preprint arXiv:1802.00892"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-022-01688-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-022-01688-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-022-01688-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T04:16:21Z","timestamp":1657858581000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-022-01688-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,31]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["1688"],"URL":"https:\/\/doi.org\/10.1007\/s10115-022-01688-3","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,31]]},"assertion":[{"value":"11 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}