{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:46:07Z","timestamp":1775245567682,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030017156","type":"print"},{"value":"9783030017163","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01716-3_28","type":"book-chapter","created":{"date-parts":[[2018,10,6]],"date-time":"2018-10-06T10:11:06Z","timestamp":1538820666000},"page":"337-347","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Joint Model for Sentiment Classification and Opinion Words Extraction"],"prefix":"10.1007","author":[{"given":"Dawei","family":"Cong","sequence":"first","affiliation":[]},{"given":"Jianhua","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Yanyan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Qin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,10,7]]},"reference":[{"key":"28_CR1","unstructured":"Bailin, W., Lu, W.: Learning latent opinions for aspect-level sentiment classification (2018)"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: 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. Association for Computational Linguistics, Baltimore, June 2014. http:\/\/www.aclweb.org\/anthology\/P14-2009","DOI":"10.3115\/v1\/P14-2009"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645\u20136649 (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"issue":"8","key":"28_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":"28_CR5","unstructured":"Kaji, N., Kitsuregawa, M.: Building lexicon for sentiment analysis from massive collection of HTML documents. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (2007)"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Kiritchenko, S., Zhu, X., Cherry, C., Mohammad, S.: NRC-Canada-2014: detecting aspects and sentiment in customer reviews. In: International Workshop on Semantic Evaluation, pp. 437\u2013442 (2014)","DOI":"10.3115\/v1\/S14-2076"},{"key":"28_CR7","unstructured":"Lakkaraju, H., Socher, R., Manning, C.D.: Aspect specific sentiment analysis using hierarchical deep learning (2014)"},{"key":"28_CR8","unstructured":"Li, F., et al.: Structure-aware review mining and summarization. In: International Conference on Computational Linguistics, pp. 653\u2013661 (2010)"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Liu, B.: Sentiment Analysis and Opinion Mining. Morgan Claypool Publishers, San Rafael (2012)","DOI":"10.1007\/978-3-031-02145-9"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Mitchell, M., Aguilar, J., Wilson, T., Durme, B.V.: Open domain targeted sentiment (2014)","DOI":"10.18653\/v1\/D13-1171"},{"key":"28_CR11","unstructured":"Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Computer Science (2013)"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Shirai, K.: PhraseRNN: phrase recursive neural network for aspect-based sentiment analysis. In: Conference on Empirical Methods in Natural Language Processing, pp. 2509\u20132514 (2015)","DOI":"10.18653\/v1\/D15-1298"},{"issue":"12","key":"28_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/1500000011","volume":"2","author":"B Pang","year":"2008","unstructured":"Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(12), 1\u2013135 (2008)","journal-title":"Found. Trends Inf. Retr."},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of International Workshop on Semantic Evaluation at (SemEval 2014), pp. 27\u201335 (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"28_CR16","unstructured":"Prez-Rosas, V.: Learning sentiment lexicons in Spanish. In: Eighth International Conference on Language Resources and Evaluation (2013)"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Qian, Q., Tian, B., Huang, M., Liu, Y., Zhu, X., Zhu, X.: Learning tag embeddings and tag-specific composition functions in recursive neural network. In: Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing, pp. 1365\u20131374 (2015)","DOI":"10.3115\/v1\/P15-1132"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Eacl 2009, Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, 30 March\u20133 April 2009, Athens, Greece, pp. 675\u2013682 (2009)","DOI":"10.3115\/1609067.1609142"},{"issue":"1","key":"28_CR19","first-page":"36","volume":"5","author":"KS Tai","year":"2015","unstructured":"Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. Comput. Sci. 5(1), 36 (2015)","journal-title":"Comput. Sci."},{"key":"28_CR20","unstructured":"Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Computer Science (2015)"},{"key":"28_CR21","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. Association for Computational Linguistics, Austin, November 2016. https:\/\/aclweb.org\/anthology\/D16-1021","DOI":"10.18653\/v1\/D16-1021"},{"key":"28_CR22","unstructured":"Vo, D.T., Zhang, Y.: Deep learning for event-driven stock prediction. In: Proceedings of IJCAI, Buenos Aires, Argentina, August 2015"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Recursive neural conditional random fields for aspect-based sentiment analysis. CoRR abs\/1603.06679 (2016). http:\/\/arxiv.org\/abs\/1603.06679","DOI":"10.18653\/v1\/D16-1059"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Conference on Empirical Methods in Natural Language Processing, pp. 606\u2013615 (2016)","DOI":"10.18653\/v1\/D16-1058"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, Y., Vo, D.T.: Gated neural networks for targeted sentiment analysis (2016)","DOI":"10.1609\/aaai.v30i1.10380"},{"key":"28_CR26","unstructured":"Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Conference on Empirical Methods in Natural Language Processing, pp. 56\u201365 (2010)"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01716-3_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:47:19Z","timestamp":1775242039000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01716-3_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030017156","9783030017163"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01716-3_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"7 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cips-cl.org\/static\/CCL2018\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"www.softconf.com","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"84","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":"33","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":"0","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":"39% - 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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}