{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:07:47Z","timestamp":1779494867169,"version":"3.53.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031039478","type":"print"},{"value":"9783031039485","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-03948-5_18","type":"book-chapter","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T10:07:17Z","timestamp":1653300437000},"page":"216-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neighborhood Network for Aspect-Based Sentiment Analysis"],"prefix":"10.1007","author":[{"given":"Huan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quansheng","family":"Dou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Hu, Z., Hu, J., Ding, W., Zheng, X.: Review sentiment analysis based on deep learning. In: 2015 IEEE 12th International Conference on e-Business Engineering, pp. 87\u201394 (2015)","DOI":"10.1109\/ICEBE.2015.24"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1\u2013167 (2012)","DOI":"10.1007\/978-3-031-02145-9_1"},{"issue":"2","key":"18_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2938640","volume":"49","author":"A Giachanou","year":"2016","unstructured":"Giachanou, A., Crestani, F.: Like it or not: a survey of twitter sentiment analysis methods. ACM Comput. Surv. (CSUR) 49(2), 1\u201341 (2016)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"8","key":"18_CR4","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"18_CR6","unstructured":"Vo, D., Zhang, Y.: Target-dependent twitter sentiment classification with rich automatic features. AAAI Press (2015)"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Chen, L., Yang, Y.: Emotional speaker recognition based on i-vector through Atom Aligned Sparse Representation. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7760\u20137764. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6639174"},{"issue":"16","key":"18_CR8","doi-asserted-by":"publisher","first-page":"21265","DOI":"10.1007\/s11042-017-5529-5","volume":"77","author":"H Han","year":"2018","unstructured":"Han, H., Zhang, J., Yang, J., Shen, Y., Zhang, Y.: Generate domain-specific sentiment lexicon for review sentiment analysis. Multimedia Tools Appl. 77(16), 21265\u201321280 (2018). https:\/\/doi.org\/10.1007\/s11042-017-5529-5","journal-title":"Multimedia Tools Appl."},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Mikolov, T., Karafi\u00e1t, M., Burget, L., Cernock\u00fd, J., Khudanpur, S.: Recurrent neural network-based language model. In: Interspeech, pp. 1045\u20131048 (2010)","DOI":"10.21437\/Interspeech.2010-343"},{"key":"18_CR10","unstructured":"Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298\u20133307 (2016)"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Ruder, S., Ghaffari, P., Breslin, J.G.: A hierarchical model of reviews for aspect-based sentiment analysis. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 999\u20131005 (2016)","DOI":"10.18653\/v1\/D16-1103"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. In: IJCAI 2017 Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 4068\u20134074 (2017)","DOI":"10.24963\/ijcai.2017\/568"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Tay, Y., Tuan, L.A., Hui, S.C.: Dyadic memory networks for aspect-based sentiment analysis. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 107\u2013116 (2017)","DOI":"10.1145\/3132847.3132936"},{"key":"18_CR14","doi-asserted-by":"publisher","first-page":"8762","DOI":"10.1109\/ACCESS.2021.3049294","volume":"9","author":"Y Lin","year":"2021","unstructured":"Lin, Y., Wang, C., Song, H., et al.: Multi-head self-attention transformation networks for aspect-based sentiment analysis. IEEE Access 9, 8762\u20138770 (2021)","journal-title":"IEEE Access"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Huang, B., Carley, K.: Parameterized convolutional neural networks for aspect level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1091\u20131096. Association for Computational Linguistics, Brussels (2018)","DOI":"10.18653\/v1\/D18-1136"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Fan, C., Gao, Q., Du, J., et al.: Convolution-based memory network for aspect-based sentiment analysis. In: The 41st International ACM SIGIR Conference on Research & Development In Information Retrieval, pp. 1161\u20131164. ACM, New York (2018)","DOI":"10.1145\/3209978.3210115"},{"issue":"12","key":"18_CR17","first-page":"2583","volume":"57","author":"C Yan","year":"2020","unstructured":"Yan, C., Leibo, Y., Guanghe, Z., et al.: Text sentiment orientation analysis of multi-channels CNN and BIGRU based on attention mechanism. J. Comput. Res. Dev. 57(12), 2583\u20132595 (2020)","journal-title":"J. Comput. Res. Dev."},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Pavlopoulos, J., et al.: SemEval-2014 Task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27\u201335 (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Dong, L., Wei, F., Tan, C., et al.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49\u201354 (2014)","DOI":"10.3115\/v1\/P14-2009"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606\u2013615(2016)","DOI":"10.18653\/v1\/D16-1058"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 214\u2013224 (2016)","DOI":"10.18653\/v1\/D16-1021"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946\u2013956 (2018)","DOI":"10.18653\/v1\/P18-1087"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Chen, Z., Qian, T.: Transfer capsule network for aspect level sentiment classification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 547\u2013556 (2019)","DOI":"10.18653\/v1\/P19-1052"}],"container-title":["IFIP Advances in Information and Communication Technology","Intelligent Information Processing XI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-03948-5_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:03:33Z","timestamp":1779494613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-03948-5_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031039478","9783031039485"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-03948-5_18","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.intsci.ac.cn\/iip2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 reviews per paper","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}