{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T20:58:22Z","timestamp":1760821102715,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322328"},{"type":"electronic","value":"9783030322335"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32233-5_50","type":"book-chapter","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T22:04:51Z","timestamp":1569967491000},"page":"647-659","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Implicit Objective Network for Emotion Detection"],"prefix":"10.1007","author":[{"given":"Hao","family":"Fei","sequence":"first","affiliation":[]},{"given":"Yafeng","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Donghong","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"50_CR1","unstructured":"Bahuleyan, H., Mou, L., Vechtomova, O., Poupart, P.: Variational attention for sequence-to-sequence models. arXiv preprint arXiv:1712.08207 (2017)"},{"issue":"4","key":"50_CR2","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.dss.2012.05.024","volume":"53","author":"A Balahur","year":"2012","unstructured":"Balahur, A., Hermida, J.M., Montoyo, A.: Detecting implicit expressions of emotion in text: a comparative analysis. Decis. Support Syst. 53(4), 742\u2013753 (2012)","journal-title":"Decis. Support Syst."},{"key":"50_CR3","doi-asserted-by":"crossref","unstructured":"Balazs, J.A., Marrese-Taylor, E., Matsuo, Y.: IIIDYT at IEST 2018: implicit emotion classification with deep contextualized word representations. arXiv preprint arXiv:1808.08672 (2018)","DOI":"10.18653\/v1\/W18-6208"},{"key":"50_CR4","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349 (2015)","DOI":"10.18653\/v1\/K16-1002"},{"key":"50_CR5","unstructured":"Goyal, A.G.A.P., Sordoni, A., C\u00f4t\u00e9, M.A., Ke, N.R., Bengio, Y.: Z-forcing: training stochastic recurrent networks. In: Proceedings of Advances in Neural Information Processing Systems, pp. 6713\u20136723 (2017)"},{"key":"50_CR6","doi-asserted-by":"crossref","unstructured":"Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168\u2013177. ACM (2004)","DOI":"10.1145\/1014052.1014073"},{"key":"50_CR7","unstructured":"Johnson, R., Zhang, T.: Supervised and semi-supervised text categorization using LSTM for region embeddings. arXiv preprint arXiv:1602.02373 (2016)"},{"key":"50_CR8","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 562\u2013570 (2017)","DOI":"10.18653\/v1\/P17-1052"},{"key":"50_CR9","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)","DOI":"10.18653\/v1\/E17-2068"},{"key":"50_CR10","doi-asserted-by":"publisher","first-page":"27124","DOI":"10.1109\/ACCESS.2019.2892624","volume":"7","author":"R Kamal","year":"2019","unstructured":"Kamal, R., Shah, M.A., Maple, C., Masood, M., Wahid, A., Mehmood, A.: Emotion classification and crowd source sensing; a lexicon based approach. IEEE Access 7, 27124\u201327134 (2019)","journal-title":"IEEE Access"},{"key":"50_CR11","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"50_CR12","doi-asserted-by":"crossref","unstructured":"Klinger, R., De Clercq, O., Mohammad, S.M., Balahur, A.: IEST: WASSA-2018 implicit emotions shared task. arXiv preprint arXiv:1809.01083 (2018)","DOI":"10.18653\/v1\/W18-6206"},{"key":"50_CR13","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"50_CR14","unstructured":"Le, H., Tran, T., Nguyen, T., Venkatesh, S.: Variational memory encoder-decoder. In: Proceedings of Advances in Neural Information Processing Systems, pp. 1508\u20131518 (2018)"},{"issue":"1","key":"50_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00416ED1V01Y201204HLT016","volume":"5","author":"B Liu","year":"2012","unstructured":"Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1\u2013167 (2012)","journal-title":"Synth. Lect. Hum. Lang. Technol."},{"key":"50_CR16","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"50_CR17","unstructured":"Miao, Y., Yu, L., Blunsom, P.: Neural variational inference for text processing. In: Proceedings of the International Conference on Machine Learning, pp. 1727\u20131736 (2016)"},{"key":"50_CR18","doi-asserted-by":"crossref","unstructured":"Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-2002 Conference on Empirical Methods in Natural Language Processing, pp. 79\u201386. Association for Computational Linguistics (2002)","DOI":"10.3115\/1118693.1118704"},{"key":"50_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-69005-6_35","volume-title":"Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data","author":"H Ren","year":"2017","unstructured":"Ren, H., Ren, Y., Li, X., Feng, W., Liu, M.: Natural logic inference for emotion detection. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds.) CCL\/NLP-NABD -2017. LNCS (LNAI), vol. 10565, pp. 424\u2013436. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-69005-6_35"},{"key":"50_CR20","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ins.2016.06.040","volume":"369","author":"Y Ren","year":"2016","unstructured":"Ren, Y., Wang, R., Ji, D.: A topic-enhanced word embedding for twitter sentiment classification. Inf. Sci. 369, 188\u2013198 (2016)","journal-title":"Inf. Sci."},{"key":"50_CR21","doi-asserted-by":"crossref","unstructured":"Ren, Y., Zhang, Y., Zhang, M., Ji, D.: Context-sensitive twitter sentiment classification using neural network. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.9974"},{"key":"50_CR22","doi-asserted-by":"crossref","unstructured":"Rozental, A., Fleischer, D., Kelrich, Z.: Amobee at IEST 2018: transfer learning from language models. arXiv preprint arXiv:1808.08782 (2018)","DOI":"10.18653\/v1\/W18-6207"},{"issue":"4","key":"50_CR23","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1177\/0539018405058216","volume":"44","author":"KR Scherer","year":"2005","unstructured":"Scherer, K.R.: What are emotions? And how can they be measured? Soc. Sci. Inf. 44(4), 695\u2013729 (2005)","journal-title":"Soc. Sci. Inf."},{"issue":"11","key":"50_CR24","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"IEEE Trans. Signal Process."},{"key":"50_CR25","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"50_CR26","unstructured":"Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., Xu, B.: Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv preprint arXiv:1611.06639 (2016)"},{"key":"50_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 207\u2013212 (2016)","DOI":"10.18653\/v1\/P16-2034"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32233-5_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:11:38Z","timestamp":1710353498000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32233-5_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322328","9783030322335"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32233-5_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"30 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dunhuang","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2019\/","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":"softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"492","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":"85","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":"56","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":"17% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}