{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:22:24Z","timestamp":1758273744964,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030594091"},{"type":"electronic","value":"9783030594107"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-59410-7_51","type":"book-chapter","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T16:57:43Z","timestamp":1600707463000},"page":"736-751","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SentiMem: Attentive Memory Networks for Sentiment Classification in User Review"],"prefix":"10.1007","author":[{"given":"Xiaosong","family":"Jia","sequence":"first","affiliation":[]},{"given":"Qitian","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Guihai","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,18]]},"reference":[{"key":"51_CR1","doi-asserted-by":"crossref","unstructured":"Cambria, E., Fu, J., Bisio, F., Poria, S.: Affectivespace 2: enabling affective intuition for concept-level sentiment analysis. In: Association for the Advancement of Artificial Intelligence (AAAI), pp. 508\u2013514 (2015)","DOI":"10.1609\/aaai.v29i1.9230"},{"key":"51_CR2","doi-asserted-by":"crossref","unstructured":"Cambria, E., Poria, S., Hazarika, D., Kwok, K.: Senticnet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Association for the Advancement of Artificial Intelligence (AAAI), pp. 1795\u20131802 (2018)","DOI":"10.1609\/aaai.v32i1.11559"},{"key":"51_CR3","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z., Zha, H.: Sequential recommendation with user memory networks. In: ACM International Conference on Web Search and Data Mining (WSDM), pp. 108\u2013116 (2018)","DOI":"10.1145\/3159652.3159668"},{"key":"51_CR4","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103\u2013111 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"key":"51_CR5","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018). http:\/\/arxiv.org\/abs\/1810.04805"},{"key":"51_CR6","doi-asserted-by":"crossref","unstructured":"Dou, Z.Y.: Capturing user and product information for document level sentiment analysis with deep memory network. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 521\u2013526 (2017)","DOI":"10.18653\/v1\/D17-1054"},{"key":"51_CR7","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-642-22309-9_5","volume-title":"Future Information Technology","author":"LK Hansen","year":"2011","unstructured":"Hansen, L.K., Arvidsson, A., Nielsen, F.A., Colleoni, E., Etter, M.: Good friends, bad news - affect and virality in Twitter. In: Park, J.J., Yang, L.T., Lee, C. (eds.) FutureTech 2011. CCIS, vol. 185, pp. 34\u201343. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-22309-9_5"},{"key":"51_CR8","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: International Conference on World Wide Web (WWW), pp. 507\u2013517 (2016)","DOI":"10.1145\/2872427.2883037"},{"issue":"8","key":"51_CR9","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":"51_CR10","doi-asserted-by":"crossref","unstructured":"Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Association for the Advancement of Artificial Intelligence (AAAI) (2014)","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"51_CR11","doi-asserted-by":"crossref","unstructured":"Jagannatha, A.N., Yu, H.: Bidirectional RNN for medical event detection in electronic health records. In: Proceedings of NAACL-HLT, pp. 473\u2013482 (2016)","DOI":"10.18653\/v1\/N16-1056"},{"key":"51_CR12","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1613\/jair.4272","volume":"50","author":"S Kiritchenko","year":"2014","unstructured":"Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. (JAIR) 50, 723\u2013762 (2014)","journal-title":"J. Artif. Intell. Res. (JAIR)"},{"issue":"8","key":"51_CR13","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30\u201337 (2009)","journal-title":"IEEE Comput."},{"key":"51_CR14","doi-asserted-by":"crossref","unstructured":"Ma, S., Sun, X., Lin, J., Ren, X.: A hierarchical end-to-end model for jointly improving text summarization and sentiment classification. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 4251\u20134257. AAAI Press (2018)","DOI":"10.24963\/ijcai.2018\/591"},{"key":"51_CR15","doi-asserted-by":"crossref","unstructured":"Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Association for the Advancement of Artificial Intelligence (AAAI), pp. 5876\u20135883 (2018)","DOI":"10.1609\/aaai.v32i1.12048"},{"key":"51_CR16","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"51_CR17","doi-asserted-by":"crossref","unstructured":"Mohammad, S.: Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words. In: Association for Computational Linguistics (ACL), pp. 174\u2013184 (2018)","DOI":"10.18653\/v1\/P18-1017"},{"key":"51_CR18","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"51_CR19","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog (2019)"},{"key":"51_CR20","doi-asserted-by":"crossref","unstructured":"Shin, B., Lee, T., Choi, J.D.: Lexicon integrated CNN models with attention for sentiment analysis. In: Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA), pp. 149\u2013158 (2017)","DOI":"10.18653\/v1\/W17-5220"},{"key":"51_CR21","unstructured":"Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 2440\u20132448 (2015)"},{"key":"51_CR22","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., Liu, T.: Learning semantic representations of users and products for document level sentiment classification. In: Association for Computational Linguistics (ACL), pp. 1014\u20131023 (2015)","DOI":"10.3115\/v1\/P15-1098"},{"key":"51_CR23","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (NIPS), pp. 5998\u20136008 (2017)"},{"key":"51_CR24","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Aspect sentiment classification with both word-level and clause-level attention networks. In: International Joint Conferences on Artificial Intelligence (IJCAI), pp. 4439\u20134445 (2018)","DOI":"10.24963\/ijcai.2018\/617"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59410-7_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:55:29Z","timestamp":1710269729000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59410-7_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030594091","9783030594107"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59410-7_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/db.pknu.ac.kr\/dasfaa2020\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"487","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":"119","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":"23","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":"24% - 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":"3.11","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":"6.81","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)"}},{"value":"15 demo papers and 4 industrial papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}