{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T19:51:25Z","timestamp":1757706685562,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030232801"},{"type":"electronic","value":"9783030232818"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-23281-8_23","type":"book-chapter","created":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T07:38:05Z","timestamp":1561016285000},"page":"286-294","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Accuracy and Efficiency of Sentiment Analysis Pipelines with UIMA"],"prefix":"10.1007","author":[{"given":"Nabeela","family":"Altrabsheh","sequence":"first","affiliation":[]},{"given":"Georgios","family":"Kontonatsios","sequence":"additional","affiliation":[]},{"given":"Yannis","family":"Korkontzelos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,21]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Altrabsheh, N., Cocea, M., Fallahkhair, S.: Sentiment analysis: towards a tool for analysing real-time students feedback. In: ICTAI 2014, pp. 419\u2013423. IEEE (2014)","DOI":"10.1109\/ICTAI.2014.70"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Batista-Navarro, R., Carter, J., Ananiadou, S.: Argo: enabling the development of bespoke workflows and services for disease annotation. Database 2016 (2016)","DOI":"10.1093\/database\/baw066"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Dridi, A., Recupero, D.R.: Leveraging semantics for sentiment polarity detection in social media. Int. J. Mach. Learn. Cybern., 1\u201311 (2017)","DOI":"10.1007\/s13042-017-0727-z"},{"issue":"3\u20134","key":"23_CR4","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1017\/S1351324904003523","volume":"10","author":"D Ferrucci","year":"2004","unstructured":"Ferrucci, D., Lally, A.: UIMA: an architectural approach to unstructured information processing in the corporate research environment. Nat. Lang. Eng. 10(3\u20134), 327\u2013348 (2004)","journal-title":"Nat. Lang. Eng."},{"key":"23_CR5","unstructured":"Go, A., Huang, L., Bhayani, R.: Twitter sentiment analysis. CS224N Project Report, Stanford (2009)"},{"issue":"11","key":"23_CR6","doi-asserted-by":"publisher","first-page":"e239","DOI":"10.2196\/jmir.2721","volume":"15","author":"F Greaves","year":"2013","unstructured":"Greaves, F., Ramirez-Cano, D., Millett, C., et al.: Use of sentiment analysis for capturing patient experience from free-text comments posted online. J. Med. Internet Res. 15(11), e239 (2013)","journal-title":"J. Med. Internet Res."},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Khuc, V.N., Shivade, C., Ramnath, R., et al.: Towards building large-scale distributed systems for Twitter sentiment analysis. In: Proceedings of SAC, pp. 459\u2013464. ACM (2012)","DOI":"10.1145\/2245276.2245364"},{"key":"23_CR8","unstructured":"Kontonatsios, G., Thompson, P., Batista-Navarro, R.T., et al.: Extending an interoperable platform to facilitate the creation of multilingual and multimodal NLP applications. In: Proceedings of ACL 2013: System Demonstrations, pp. 43\u201348 (2013)"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Kotzias, D., Denil, M., De Freitas, N., et al.: From group to individual labels using deep features. In: Proceedings of ACM SIGKDD 2015, pp. 597\u2013606. ACM (2015)","DOI":"10.1145\/2783258.2783380"},{"key":"23_CR10","unstructured":"Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: Building the state-of-the-art in sentiment analysis of Tweets. arXiv preprint arXiv:1308.6242 (2013)"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Pal, S., Ghosh, S.: Sentiment analysis using averaged histogram. Int. J. Comput. Appl. 162(12) (2017)","DOI":"10.5120\/ijca2017913421"},{"key":"23_CR12","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ins.2016.06.040","volume":"369","author":"Y Ren","year":"2016","unstructured":"Ren, Y., Wang, R., Ji, D.: A topic-enhanced word embedding for twitter sentiment classification. Inf. Sci. 369, 188\u2013198 (2016)","journal-title":"Inf. Sci."},{"key":"23_CR13","unstructured":"Rodr\u0131guez-Penagos, C., Narbona, D.G., Sanabre, G.M., et al.: Sentiment analysis and visualization using UIMA and Solr. Unstructured Information Management Architecture (UIMA), p. 42 (2013)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Rosenthal, S., Farra, N., Nakov, P.: SemEval-2017 task 4: sentiment analysis in Twitter. In: Proceedings of SemEval-2017, pp. 502\u2013518 (2017)","DOI":"10.18653\/v1\/S17-2088"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Sarker, A., Gonzalez, G.: HLP@UPenn at SemEval-2017 task 4A: a simple, self-optimizing text classification system combining dense and sparse vectors. In: Proceedings of SemEval-2017, pp. 640\u2013643 (2017)","DOI":"10.18653\/v1\/S17-2105"},{"key":"23_CR16","unstructured":"Sarma, P.K., Sethares, W.: Simple algorithms for sentiment analysis on sentiment rich, data poor domains. In: Proceedings of ACL 2018, pp. 3424\u20133435 (2018)"},{"key":"23_CR17","unstructured":"Sohn, S., Savova, G.K.: Mayo clinic smoking status classification system: extensions and improvements. In: AMIA Annual Symposium Proceedings, vol. 2009, p. 619. American Medical Informatics Association (2009)"},{"key":"23_CR18","unstructured":"UMICH: Dataset SI650 - sentiment classification (2011). https:\/\/goo.gl\/Xfr8lI"},{"issue":"3","key":"23_CR19","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.giq.2015.03.003","volume":"32","author":"SM Zavattaro","year":"2015","unstructured":"Zavattaro, S.M., French, P.E., Mohanty, S.D.: A sentiment analysis of US local government Tweets: the connection between tone and citizen involvement. Gov. Inf. Q. 32(3), 333\u2013341 (2015)","journal-title":"Gov. Inf. Q."}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-23281-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:05:54Z","timestamp":1710270354000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-23281-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030232801","9783030232818"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-23281-8_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"21 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLDB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applications of Natural Language to Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salford","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nldb2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.salford.ac.uk\/conferencing-at-salford\/conference-management\/current-conference\/nldb-conference-2019","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}