{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:48:08Z","timestamp":1742921288710,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819629107"},{"type":"electronic","value":"9789819629114"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-2911-4_21","type":"book-chapter","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T09:43:54Z","timestamp":1741599834000},"page":"220-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Violent Language Detection Model Based on Short Text"],"prefix":"10.1007","author":[{"given":"Dongfang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boya","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"21_CR1","unstructured":"Pose Estimation of Point Sets Using Residual MLP in Intelligent Transportation Infrastructure.IEEE Transactions on Intelligent Transportation Systems (2023)"},{"key":"21_CR2","doi-asserted-by":"publisher","first-page":"19651","DOI":"10.1109\/ACCESS.2024.3361404","volume":"12","author":"B Botella-Gil","year":"2024","unstructured":"Botella-Gil, B., Sep\u00falveda-Torres, R., Bonet-Jover, A., Mart\u00ednez-Barco, P., Saquete, E.: Semi-automatic dataset annotation applied to automatic violent message detection. IEEE Access 12, 19651\u201319664 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3361404","journal-title":"IEEE Access"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Wang, L., Islam, T.: Automatic detection of cyberbullying: racism and sexism on twitter. In: Jahankhani, H. (eds.) Cybersecurity in the Age of Smart Societies. Advanced Sciences and Technologies for Security Applications. Springer, Cham\u00a0 (2023)","DOI":"10.1007\/978-3-031-20160-8_7"},{"key":"21_CR4","unstructured":"Underwater Visibility Enhancement IoT System in Extreme Environment. IEEE Internet of Things Journal (2023)"},{"key":"21_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.2984991","author":"Deep Fuzzy Hashing Network for Efficient Image Retrieval","year":"2020","unstructured":"Deep Fuzzy Hashing Network for Efficient Image Retrieval: IEEE Trans. Fuzzy Syst. (2020). https:\/\/doi.org\/10.1109\/TFUZZ.2020.2984991","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.cogr.2020.12.002","volume":"1","author":"Visual information processing for deep-sea visual monitoring system","year":"2021","unstructured":"Visual information processing for deep-sea visual monitoring system: Cognitive Robotics 1, 3\u201311 (2021)","journal-title":"Cognitive Robotics"},{"key":"21_CR7","unstructured":"Learning Latent Dynamics for Autonomous Shape Control of Deformable Object. IEEE Transactions on Intelligent Transportation Systems (2022)"},{"key":"21_CR8","unstructured":"Brain-Inspired Perception Feature and Cognition Model Applied to Safety Patrol Robot. IEEE Transactions on Industrial Informatics (2023)"},{"issue":"11","key":"21_CR9","doi-asserted-by":"publisher","first-page":"11421","DOI":"10.1109\/TVT.2022.3189410","volume":"71","author":"J Li","year":"2022","unstructured":"Li, J., Li, S., Cheng, L., et al.: BSAS: a blockchain-based trustworthy and privacy-preserving speed advisory system. IEEE Trans. Veh. Technol. 71(11), 11421\u201311430 (2022)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"10","key":"21_CR10","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1177\/21650799231176078","volume":"71","author":"HD Byon","year":"2023","unstructured":"Byon, H.D., Harris, C., Crandall, M., Song, J., Topaz, M.: Identifying Type II workplace violence from clinical notes using natural language processing. Workplace Health Safety. 71(10), 484\u2013490 (2023)","journal-title":"Workplace Health Safety."},{"issue":"03","key":"21_CR11","doi-asserted-by":"publisher","first-page":"894","DOI":"10.16208\/j.issn1000-7024.2023.03.035","volume":"44","author":"L Yan","year":"2023","unstructured":"Yan, L., Chao, L., Zhongxiong, L., Wentao, L.: Aspect level sentiment analysis using fused multi attention neural networks. Comput. Eng. Design 44(03), 894\u2013900 (2023). https:\/\/doi.org\/10.16208\/j.issn1000-7024.2023.03.035","journal-title":"Comput. Eng. Design"},{"key":"21_CR12","doi-asserted-by":"publisher","first-page":"77820","DOI":"10.1109\/ACCESS.2020.2990306","volume":"8","author":"CR Aydin","year":"2020","unstructured":"Aydin, C.R., Gungor, T.: Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations. IEEE Access 8, 77820\u201377832 (2020)","journal-title":"IEEE Access"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., Yin, C., Zhong, S.-H.: Sentence constituent-aware aspect category sentiment analysis with graph attention networks. Nat. Lang. Process. Chin. Comput.\u00a012430, 815\u2013827 (2020)","DOI":"10.1007\/978-3-030-60450-9_64"},{"issue":"6","key":"21_CR14","doi-asserted-by":"publisher","first-page":"4117","DOI":"10.1007\/s12652-020-01791-9","volume":"12","author":"K Sangeetha","year":"2021","unstructured":"Sangeetha, K., Prabha, D.: Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM. J. Ambient. Intell. Humaniz. Comput. 12(6), 4117\u20134126 (2021)","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Yin, D., Meng, T., Chang, K.W.: SentiBERT: a transferable transformer-based architecture for compositional sentiment semantics. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3695\u20133706. Association for Computational Linguistics,\u00a0Stroudsburg, PA (2020)","DOI":"10.18653\/v1\/2020.acl-main.341"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., et al.: Modeling relational data with graph convolutional networks. In: The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, 3\u20137 June\u00a0 Proceedings 15, pp. 593\u2013607. Springer International Publishing,\u00a0 \u00a0(2018)","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"21_CR18","first-page":"16138","volume":"35","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Mishra, P., Franceschi, L., et al.: Refactor gnns: revisiting factorisation-based models from a message-passing perspective. Adv. Neural. Inf. Process. Syst. 35, 16138\u201316150 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, T., Yang, C., Li, Y., et al.: Space4hgnn: a novel, modularized and reproducible platform to evaluate heterogeneous graph neural network. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2776\u20132789 (2022)","DOI":"10.1145\/3477495.3531720"},{"key":"21_CR20","first-page":"38436","volume":"35","author":"H Ahn","year":"2022","unstructured":"Ahn, H., Yang, Y., Gan, Q., et al.: Descent steps of a relation-aware energy produce heterogeneous graph neural networks. Adv. Neural. Inf. Process. Syst. 35, 38436\u201338448 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Wang, X., Ji, H., Shi, C., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference, pp. 2022\u20132032 (2019)","DOI":"10.1145\/3308558.3313562"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2911-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T09:44:03Z","timestamp":1741599843000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2911-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819629107","9789819629114"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2911-4_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"11 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISAIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Artificial Intelligence and Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isair2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/isair.site\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}