{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:33:20Z","timestamp":1743136400539,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819923557"},{"type":"electronic","value":"9789819923564"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-2356-4_16","type":"book-chapter","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T13:02:49Z","timestamp":1683896569000},"page":"194-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine-Grained Sentiment Analysis of\u00a0Online-Offline Danmaku Based on\u00a0CNN and\u00a0Attention"],"prefix":"10.1007","author":[{"given":"Yan","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","unstructured":"Cha, J., Lee, J.: Extracting topic related keywords by backtracking CNN based text classifier. In: 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), Toyama, Japan, December 5\u20138, 2018. pp. 93\u201396 (2018). https:\/\/doi.org\/10.1109\/SCIS-ISIS.2018.00026","DOI":"10.1109\/SCIS-ISIS.2018.00026"},{"key":"16_CR2","doi-asserted-by":"publisher","unstructured":"Chen, Z., Tang, Y., Zhang, Z., Zhang, C., Wang, L.: Sentiment-aware short text classification based on convolutional neural network and attention. In: 31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019, Portland, OR, USA, November 4\u20136, pp. 1172\u20131179 (2019). https:\/\/doi.org\/10.1109\/ICTAI.2019.00162","DOI":"10.1109\/ICTAI.2019.00162"},{"key":"16_CR3","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":"16_CR4","doi-asserted-by":"publisher","unstructured":"Gan, C., Feng, Q., Zhang, Z.: Scalable multi-channel dilated cnn-bilstm model with attention mechanism for chinese textual sentiment analysis. Future Generation Computer Systems, vol. 118, pp. 297\u2013309 (2021). https:\/\/doi.org\/10.1016\/j.future.2021.01.024 https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X21000340","DOI":"10.1016\/j.future.2021.01.024"},{"key":"16_CR5","doi-asserted-by":"publisher","unstructured":"Graves, A., Fern\u00e1ndez, S., Schmidhuber, J.: Bidirectional LSTM networks for improved phoneme classification and recognition. In: Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, 15th International Conference, Warsaw, Poland, September, pp. 11\u201315, 2005, Proceedings, Part II, pp. 799\u2013804 (2005). https:\/\/doi.org\/10.1007\/11550907_126.","DOI":"10.1007\/11550907_126"},{"key":"16_CR6","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014(October), pp. 25\u201329, 2014. Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1746\u20131751 (2014). https:\/\/www.aclweb.org\/anthology\/D14-1181\/"},{"key":"16_CR7","doi-asserted-by":"publisher","unstructured":"Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP 2003), October 23\u201325, 2003, Sanibel Island, FL, USA, pp. 70\u201377 (2003). https:\/\/doi.org\/10.1145\/945645.945658","DOI":"10.1145\/945645.945658"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"22260","DOI":"10.1109\/ACCESS.2022.3149482","volume":"10","author":"R Obiedat","year":"2022","unstructured":"Obiedat, R., et al.: Sentiment analysis of customers \u2018reviews using a hybrid evolutionary svm-based approach in an imbalanced data distribution. IEEE Access 10, 22260\u201322273 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3149482","journal-title":"IEEE Access"},{"key":"16_CR9","doi-asserted-by":"publisher","unstructured":"Sun, Y., Li, J., Zhen, Y., Tang, Y., Hu, Q., He, L.: Usee: An online-offline hybird danmaku social system. In: 22nd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018, Nanjing, China, May 9\u201311, 2018, pp. 253\u2013258 (2018). https:\/\/doi.org\/10.1109\/CSCWD.2018.8465286","DOI":"10.1109\/CSCWD.2018.8465286"},{"key":"16_CR10","doi-asserted-by":"publisher","first-page":"21517","DOI":"10.1109\/ACCESS.2022.3152828","volume":"10","author":"KL Tan","year":"2022","unstructured":"Tan, K.L., Lee, C.P., Anbananthen, K.S.M., Lim, K.M.: Roberta-lstm: a hybrid model for sentiment analysis with transformer and recurrent neural network. IEEE Access 10, 21517\u201321525 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3152828","journal-title":"IEEE Access"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Tang, Y.,et al.: Is danmaku an effective way for promoting event based social network? In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017, Portland, OR, USA, February 25 - March 1, 2017, Companion Volume, pp. 319\u2013322 (2017), http:\/\/dl.acm.org\/citation.cfm?id=3026347","DOI":"10.1145\/3022198.3026347"},{"key":"16_CR12","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, EMNLP 2016, Austin, Texas, USA, November 1\u20134(2016), pp. 606\u2013615, 2016. https:\/\/www.aclweb.org\/anthology\/D16-1058\/","DOI":"10.18653\/v1\/D16-1058"},{"key":"16_CR13","doi-asserted-by":"publisher","unstructured":"Wu, S., Liu, Y., Zou, Z., Weng, T.H.: S_i_lstm: stock price prediction based on multiple data sources and sentiment analysis. Connection Science 34(1), 44\u201362 (2022). https:\/\/doi.org\/10.1080\/09540091.2021.1940101","DOI":"10.1080\/09540091.2021.1940101"},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/978-3-030-88480-2_58","volume-title":"Natural Lang. Process. Chinese Comput.","author":"Y Zhong","year":"2021","unstructured":"Zhong, Y., Zhang, Z., Zhang, W., Zhu, J.: Bert-kg: a short text classification model based on knowledge graph and deep semantics. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds.) Natural Lang. Process. Chinese Comput., pp. 721\u2013733. Springer International Publishing, Cham (2021)"},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"149077","DOI":"10.1109\/ACCESS.2021.3118537","volume":"9","author":"Q Zhu","year":"2021","unstructured":"Zhu, Q., Jiang, X., Ye, R.: Sentiment analysis of review text based on bigru-attention and hybrid cnn. IEEE Access 9, 149077\u2013149088 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3118537","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Computer Supported Cooperative Work and Social Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-2356-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T13:05:18Z","timestamp":1683896718000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-2356-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819923557","9789819923564"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-2356-4_16","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ChineseCSCW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF Conference on Computer Supported Cooperative Work  and Social Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Datong","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":"23 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","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":"chinesecscw2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.scholat.com\/ccscw\/2022","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","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":"60","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":"30","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":"28% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}