{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:13:59Z","timestamp":1743009239005,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031446924"},{"type":"electronic","value":"9783031446931"}],"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\/978-3-031-44693-1_36","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T08:02:39Z","timestamp":1696665759000},"page":"456-468","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ECOD: A Multi-modal Dataset for\u00a0Intelligent Adjudication of\u00a0E-Commerce Order Disputes"],"prefix":"10.1007","author":[{"given":"Liyi","family":"Chen","sequence":"first","affiliation":[]},{"given":"Shuaipeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hailei","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lijie","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Guanglu","family":"Wan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Chen, C., Teng, Z., Wang, Z., Zhang, Y.: Discrete opinion tree induction for aspect-based sentiment analysis. In: ACL, pp. 2051\u20132064 (2022)","DOI":"10.18653\/v1\/2022.acl-long.145"},{"issue":"3","key":"36_CR2","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1287\/mnsc.1070.0810","volume":"54","author":"Y Chen","year":"2008","unstructured":"Chen, Y., Xie, J.: Online consumer review: word-of-mouth as a new element of marketing communication mix. Manage. Sci. 54(3), 477\u2013491 (2008)","journal-title":"Manage. Sci."},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"36_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: ACL, pp. 4171\u20134186 (2019)"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: Exploiting document knowledge for aspect-level sentiment classification. In: ACL, pp. 579\u2013585 (2018)","DOI":"10.18653\/v1\/P18-2092"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Jindal, N., Liu, B.: Opinion spam and analysis. In: WSDM, pp. 219\u2013230 (2008)","DOI":"10.1145\/1341531.1341560"},{"key":"36_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, Z., Liu, B., Wei, X., Shao, J.: Spotting fake reviews via collective positive-unlabeled learning. In: ICDM, pp. 899\u2013904 (2014)","DOI":"10.1109\/ICDM.2014.47"},{"key":"36_CR10","unstructured":"Maas, A., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: ACL, pp. 142\u2013150 (2011)"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: ACM Conference on Recommender Systems, pp. 165\u2013172 (2013)","DOI":"10.1145\/2507157.2507163"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"McAuley, J.J., Leskovec, J.: From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: WWW, pp. 897\u2013908 (2013)","DOI":"10.1145\/2488388.2488466"},{"key":"36_CR13","doi-asserted-by":"publisher","first-page":"65771","DOI":"10.1109\/ACCESS.2021.3075573","volume":"9","author":"R Mohawesh","year":"2021","unstructured":"Mohawesh, R., et al.: Fake reviews detection: a survey. IEEE Access 9, 65771\u201365802 (2021)","journal-title":"IEEE Access"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: WWW, pp. 191\u2013200 (2012)","DOI":"10.1145\/2187836.2187863"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.: What yelp fake review filter might be doing? In: AAAI, vol. 7 (2013)","DOI":"10.1609\/icwsm.v7i1.14389"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Nilizadeh, S., Aghakhani, H., Gustafson, E., Kruegel, C., Vigna, G.: Lightning talk - think outside the dataset: finding fraudulent reviews using cross-dataset analysis. In: WWW, pp. 1288\u20131289 (2019)","DOI":"10.1145\/3308558.3313647"},{"key":"36_CR17","unstructured":"Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: ACL, pp. 309\u2013319 (2011)"},{"key":"36_CR18","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.knosys.2015.06.015","volume":"89","author":"K Ravi","year":"2015","unstructured":"Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl. Based Syst. 89, 14\u201346 (2015)","journal-title":"Knowl. Based Syst."},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Rayana, S., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: SIGKDD, pp. 985\u2013994 (2015)","DOI":"10.1145\/2783258.2783370"},{"key":"36_CR20","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.ins.2015.03.040","volume":"311","author":"J Serrano-Guerrero","year":"2015","unstructured":"Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., Herrera-Viedma, E.: Sentiment analysis: a review and comparative analysis of web services. Inf. Sci. 311, 18\u201338 (2015)","journal-title":"Inf. Sci."},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Sharma, A., Cosley, D.: Do social explanations work? Studying and modeling the effects of social explanations in recommender systems. In: WWW, pp. 1133\u20131144 (2013)","DOI":"10.1145\/2488388.2488487"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Srujan, K., Nikhil, S., Raghav Rao, H., Karthik, K., Harish, B., Keerthi Kumar, H.: Classification of amazon book reviews based on sentiment analysis. In: INDIA, pp. 401\u2013411 (2018)","DOI":"10.1007\/978-981-10-7512-4_40"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Sun, K., Zhang, R., Mensah, S., Mao, Y., Liu, X.: Aspect-level sentiment analysis via convolution over dependency tree. In: EMNLP-IJCNLP (2019)","DOI":"10.18653\/v1\/D19-1569"},{"key":"36_CR24","unstructured":"Wolf, T., et al.: HuggingFace\u2019s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)"},{"key":"36_CR25","doi-asserted-by":"publisher","first-page":"109438","DOI":"10.1016\/j.asoc.2022.109438","volume":"128","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Dong, Y., Wu, H., Song, H., Deng, S., Chen, Y.: Metapath and syntax-aware heterogeneous subgraph neural networks for spam review detection. Appl. Soft Comput. 128, 109438 (2022)","journal-title":"Appl. Soft Comput."}],"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\/978-3-031-44693-1_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T08:23:08Z","timestamp":1696839788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44693-1_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031446924","9783031446931"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44693-1_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","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":"Foshan","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2023\/index.php","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":"478","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":"143","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":"30% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}