{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:49:23Z","timestamp":1742982563470,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030732790"},{"type":"electronic","value":"9783030732806"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-73280-6_27","type":"book-chapter","created":{"date-parts":[[2021,4,4]],"date-time":"2021-04-04T08:02:28Z","timestamp":1617523348000},"page":"340-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Empirical Study of Tweets Topic Classification Using Transformer-Based Language Models"],"prefix":"10.1007","author":[{"given":"Ranju","family":"Mandal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susanne","family":"Becken","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bela","family":"Stantic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"issue":"2","key":"27_CR1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1177\/0047287517747753","volume":"58","author":"AR Alaei","year":"2019","unstructured":"Alaei, A.R., Becken, S., Stantic, B.: Sentiment analysis in tourism: capitalizing on big data. J. Travel Res. 58(2), 175\u2013191 (2019)","journal-title":"J. Travel Res."},{"key":"27_CR2","unstructured":"Allan, J.: Introduction to topic detection and tracking. The Information Retrieval Series, vol. 12 (2012)"},{"key":"27_CR3","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.ecoinf.2019.05.001","volume":"52","author":"S Becken","year":"2019","unstructured":"Becken, S., Connolly, R.M., Chen, J., Stantic, B.: A hybrid is born: integrating collective sensing, citizen science and professional monitoring of the environment. Ecol. Inform. 52, 35\u201345 (2019)","journal-title":"Ecol. Inform."},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Becken, S., Stantic, B., Chen, J., Alaei, A., Connolly, R.M.: Monitoring the environment and human sentiment on the great barrier reef: assessing the potential of collective sensing. J. Environ. Manag. 203, 87\u201397 (2017)","DOI":"10.1016\/j.jenvman.2017.07.007"},{"key":"27_CR5","unstructured":"Chen, M., Jin, X., Shen, D.: Short text classification improved by learning multi-granularity topics. In: IJCAI (2011)"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Dai, Z., et al.: Crest: cluster-based representation enrichment for short text classification. In: Advances in Knowledge Discovery and Data Mining, pp. 256\u2013267 (2013)","DOI":"10.1007\/978-3-642-37456-2_22"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Daume, S., Galaz, V.: \u201cAnyone know what species this is?\u201d - twitter conversations as embryonic citizen science communities. Plos One 11, 1\u201325 (2016)","DOI":"10.1371\/journal.pone.0151387"},{"key":"27_CR8","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":"27_CR9","unstructured":"Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. CoRR abs\/1801.06146 (2018). http:\/\/arxiv.org\/abs\/1801.06146"},{"key":"27_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: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, pp. 216\u2013225 (2014)","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Kumar, A., Jaiswal, A.: Systematic literature review of sentiment analysis on twitter using soft computing techniques. Concurrency and Computation: Practice and Experience, vol. 32, no. 1 (2019)","DOI":"10.1002\/cpe.5107"},{"key":"27_CR12","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: a lite bert for self-supervised learning of language representations (2019)"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Lee, K., Palsetia, D., Narayanan, R., Patwary, M.M.A., Agrawal, A., Choudhary, A.: Twitter trending topic classification. In: International Conference on Data Mining Workshops, pp. 251\u2013258 (2011)","DOI":"10.1109\/ICDMW.2011.171"},{"key":"27_CR14","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019). http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"27_CR15","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.ocecoaman.2018.03.029","volume":"158","author":"L Lodia","year":"2018","unstructured":"Lodia, L., Tardin, R.: Citizen science contributes to the understanding of the occurrence and distribution of cetaceans in south-eastern brazil - a case study. Ocean Coast. Manag. 158, 45\u201355 (2018)","journal-title":"Ocean Coast. Manag."},{"key":"27_CR16","unstructured":"Nigam, K., Mccallum, A.K., Thrun, S.: Text classification from labeled and unlabeled documents using EM. Mach. Learn. 39(2), 103\u2013134 (2000)"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. In: Proceedings of NAACL (2018)","DOI":"10.18653\/v1\/N18-1202"},{"key":"27_CR18","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Ribeiro, F.N., Ara\u00fajo, M., Gon\u00e7alves, P., Benevenuto, F., Gon\u00e7alves, M.A.: A benchmark comparison of state-of-the-practice sentiment analysis methods. CoRR abs\/1512.01818 (2015)","DOI":"10.1140\/epjds\/s13688-016-0085-1"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 841\u2013842 (2010)","DOI":"10.1145\/1835449.1835643"},{"key":"27_CR21","unstructured":"Tang, D., Qin, B., Liu, T.: Deep learning for sentiment analysis: successful approaches and future challenges. WIREs Data Min. Knowl. Disc. 5(6), 292\u2013303 (2015)"},{"key":"27_CR22","unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017). http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"27_CR23","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1016\/j.eswa.2014.09.031","volume":"42","author":"DT Vo","year":"2015","unstructured":"Vo, D.T., Ock, C.Y.: Learning to classify short text from scientific documents using topic models with various types of knowledge. Expert Syst. Appl. 42, 1684\u20131698 (2015). https:\/\/doi.org\/10.1016\/j.eswa.2014.09.031","journal-title":"Expert Syst. Appl."},{"key":"27_CR24","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J.G., Salakhutdinov, R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding. CoRR abs\/1906.08237 (2019). http:\/\/arxiv.org\/abs\/1906.08237"},{"key":"27_CR25","doi-asserted-by":"publisher","unstructured":"Y\u00fcksel, A.E., T\u00fcrkmen, Y.A., \u00d6zg\u00fcr, A., Alt\u0131nel, B.: Turkish tweet classification with transformer encoder. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp. 1380\u20131387. INCOMA Ltd. (2019). https:\/\/doi.org\/10.26615\/978-954-452-056-4_158","DOI":"10.26615\/978-954-452-056-4_158"}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73280-6_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T09:31:49Z","timestamp":1671787909000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-73280-6_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030732790","9783030732806"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73280-6_27","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":"5 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Phuket","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}