{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:03:50Z","timestamp":1743134630957,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030676636"},{"type":"electronic","value":"9783030676643"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-67664-3_38","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T07:06:46Z","timestamp":1614150406000},"page":"633-649","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hierarchical Interaction Networks with Rethinking Mechanism for Document-Level Sentiment Analysis"],"prefix":"10.1007","author":[{"given":"Lingwei","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dou","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuehai","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jizhong","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songlin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"38_CR1","unstructured":"Wang, W., Wang, H.: The influence of aspect-based opinions on user\u2019s purchase intention using sentiment analysis of online reviews. Syst. Eng. 36(1), 63\u201376 (2016)"},{"key":"38_CR2","doi-asserted-by":"crossref","unstructured":"Hyun, D., Park, C., Yang, M.C., Song, I., Lee, J.T., Yu, H.: Review sentiment-guided scalable deep recommender system. In: SIGIR, pp. 965\u2013968 (2018)","DOI":"10.1145\/3209978.3210111"},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Yuan, C., Ma, Q., Zhou, W., Han, J., Hu, S.: Jointly embedding the local and global relations of heterogeneous graph for rumor detection. In: ICDM. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00090"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Yuan, C., Zhou, W., Ma, Q., Lv, S., Han, J., Hu, S.: Learning review representations from user and product level information for spam detection. In: ICDM. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00188"},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746\u20131751. Doha, Qatar, Oct 2014","DOI":"10.3115\/v1\/D14-1181"},{"key":"38_CR6","unstructured":"Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics, pp. 3298\u20133307. Osaka, Japan, Dec 2016"},{"key":"38_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL, pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019"},{"key":"38_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/978-3-030-32381-3_16","volume-title":"Chinese Computational Linguistics","author":"C Sun","year":"2019","unstructured":"Sun, C., Qiu, X., Xu, Y., Huang, X.: How to fine-tune BERT for text classification? In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds.) CCL 2019. LNCS (LNAI), vol. 11856, pp. 194\u2013206. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32381-3_16"},{"key":"38_CR9","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: NAACL, pp. 1480\u20131489. San Diego, California, June 2016","DOI":"10.18653\/v1\/N16-1174"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Pergola, G., Gui, L., He, Y.: TDAM: a topic-dependent attention model for sentiment analysis. Inf. Process. Manage. 56(6), 102084 (2019)","DOI":"10.1016\/j.ipm.2019.102084"},{"key":"38_CR11","unstructured":"Remy, J., Tixier, A.J., Vazirgiannis, M.: Bidirectional context-aware hierarchical attention network for document understanding (2019)"},{"issue":"9","key":"38_CR12","first-page":"1774","volume":"4","author":"H Vikrant","year":"2013","unstructured":"Vikrant, H., Mukta, T.: Real time tweet summarization and sentiment analysis of game tournament. Int. J. Sci. Res. 4(9), 1774\u20131780 (2013)","journal-title":"Int. J. Sci. Res."},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Mane, V.L., Panicker, S.S., Patil, V.B.: Summarization and sentiment analysis from user health posts. In: ICPC, pp. 1\u20134. IEEE (2015)","DOI":"10.1109\/PERVASIVE.2015.7087087"},{"key":"38_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: IJCAI, pp. 4251\u20134257. International Joint Conferences on Artificial Intelligence Organization, July 2018","DOI":"10.24963\/ijcai.2018\/591"},{"key":"38_CR15","unstructured":"Wang, H., Ren, J.: A self-attentive hierarchical model for jointly improving text summarization and sentiment classification. In: ACML, pp. 630\u2013645. PMLR, Nov 2018"},{"key":"38_CR16","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.patcog.2018.01.015","volume":"79","author":"X Li","year":"2018","unstructured":"Li, X., Jie, Z., Feng, J., Liu, C., Yan, S.: Learning with rethinking: recurrently improving convolutional neural networks through feedback. Pattern Recognit. 79, 183\u2013194 (2018)","journal-title":"Pattern Recognit."},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"Gui, T., Ma, R., Zhang, Q., Zhao, L., Jiang, Y., Huang, X.: Cnn-based chinese NER with lexicon rethinking. In: IJCAI, pp. 4982\u20134988. Macao, China (2019)","DOI":"10.24963\/ijcai.2019\/692"},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: EMNLP, pp. 1422\u20131432. Association for Computational Linguistics, Lisbon, Portugal, Sep 2015","DOI":"10.18653\/v1\/D15-1167"},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Xu, J., Chen, D., Qiu, X., Huang, X.: Cached long short-term memory neural networks for document-level sentiment classification. In: EMNLP, pp. 1660\u20131669. The Association for Computational Linguistics (2016)","DOI":"10.18653\/v1\/D16-1172"},{"key":"38_CR20","doi-asserted-by":"crossref","unstructured":"Song, J.: Distilling knowledge from user information for document level sentiment classification. In: 35th IEEE International Conference on Data Engineering Workshops, pp. 169\u2013176. IEEE (2019)","DOI":"10.1109\/ICDEW.2019.00-15"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. In: EMNLP, pp. 379\u2013389. The Association for Computational Linguistics (2015)","DOI":"10.18653\/v1\/D15-1044"},{"key":"38_CR22","doi-asserted-by":"crossref","unstructured":"Bhargava, R., Sharma, Y.: Msats: multilingual sentiment analysis via text summarization. In: 7th International Conference on Cloud Computing, pp. 71\u201376. IEEE, Jan 2017","DOI":"10.1109\/CONFLUENCE.2017.7943126"},{"key":"38_CR23","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: WWW, pp. 507\u2013517. International World Wide Web Conferences Steering Committee (2016)","DOI":"10.1145\/2872427.2883037"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67664-3_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T23:05:33Z","timestamp":1740351933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67664-3_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030676636","9783030676643"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67664-3_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","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":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd2020.net\/","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":"945","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":"195","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":"21% - 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":"4,5","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":"4,4","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":"The conference took place virtually due to the COVID-19 pandemic","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)"}}]}}