{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T10:27:38Z","timestamp":1758968858540,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,9,18]],"date-time":"2017-09-18T00:00:00Z","timestamp":1505692800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"SJSU","award":["2015\/16 RSCA","2015\/16 RSCA"],"award-info":[{"award-number":["2015\/16 RSCA","2015\/16 RSCA"]}]},{"DOI":"10.13039\/501100004181","name":"Nokia Foundation","doi-asserted-by":"publisher","award":["2016-01"],"award-info":[{"award-number":["2016-01"]}],"id":[{"id":"10.13039\/501100004181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1007\/s13721-017-0159-4","type":"journal-article","created":{"date-parts":[[2017,9,18]],"date-time":"2017-09-18T00:14:27Z","timestamp":1505693667000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["On adverse drug event extractions using twitter sentiment analysis"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8313-6645","authenticated-orcid":false,"given":"Melody","family":"Moh","sequence":"first","affiliation":[]},{"given":"Teng-Sheng","family":"Moh","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,9,18]]},"reference":[{"key":"159_CR1","unstructured":"Agency for Healthcare Research and Quality (2001) Reducing and preventing adverse drug events to decrease hospital costs: Research in Action, Issue 1, March 2001. http:\/\/archive.ahrq.gov\/research\/findings\/factsheets\/errors-safety\/aderia\/ade.html . Accessed 21 Mar 2015"},{"key":"159_CR2","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo M, Gon\u00e7alves P, Cha M, Benevenuto F (2014) iFeel: a system that compares and combines sentiment analysis methods. Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea, April 2014, pp 75\u201378","DOI":"10.1145\/2567948.2577013"},{"key":"159_CR3","unstructured":"Aronson A. R. (2001) Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proceedings of the AMIA Symposium, pp 17\u201321"},{"key":"159_CR4","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/978-3-319-09339-0_62","volume-title":"Intelligent computing methodologies","author":"Y Bao","year":"2014","unstructured":"Bao Y, Quan C, Wang L, Ren F (2014) The role of pre-processing in Twitter sentiment analysis. In: Huang D-S, Jo K-H, Wang L (eds) Intelligent computing methodologies. Springer International Publishing, Switzerland, pp 615\u2013634"},{"key":"159_CR5","unstructured":"Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. Proceedings of the 23rd International Conference on Computational Linguistics, Beijing, China, August 2010, pp 36\u201344"},{"key":"159_CR6","doi-asserted-by":"crossref","unstructured":"Bian J, Topaloglu U, Yu F (2012) Towards large-scale twitter mining for drug-related adverse events. Proc. of the 2012 ACM International Workshop on Smart Health and Wellbeing, Maui, HI, October 2012, pp 25\u201332","DOI":"10.1145\/2389707.2389713"},{"key":"159_CR7","doi-asserted-by":"crossref","unstructured":"Bravo-Marquez F, Mendoza M, Poblete B (2013) Combining strengths, emotions and polarities for boosting Twitter sentiment analysis. Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, Chicago, IL, August 2013","DOI":"10.1145\/2502069.2502071"},{"key":"159_CR8","unstructured":"Edwards J (2016) Leaked Twitter API data shows the number of tweets is in serious decline. Business Insider. Twitter Usage Statistics. http:\/\/www.businessinsider.com\/tweets-on-twitter-is-in-serious-decline-2016-2 . Accessed 2 Feb 2016"},{"key":"159_CR9","unstructured":"Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision, CS224\u00a0N Project Report, Stanford University"},{"key":"159_CR10","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves P, Ara\u00fajo M, Benevenuto F, Cha M (2013) Comparing and combining sentiment analysis methods. Proceedings of the first ACM conference on Online social networks, Boston, MA, October 2013, pp 27\u201338","DOI":"10.1145\/2512938.2512951"},{"key":"159_CR11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/978-3-642-22309-9_5","volume-title":"Future information technology","author":"LK Hansen","year":"2011","unstructured":"Hansen LK, Arvidsson A, Nielsen FA, Colleoni E, Etter M (2011) Good friends, bad news-affect and virality in twitter. In: Park JJ, Yang LT, Lee C (eds) Future information technology. Springer, Berlin, pp 34\u201343"},{"key":"159_CR12","doi-asserted-by":"crossref","unstructured":"Hsu D, Moh M, Moh T-S (2017) Mining frequency of drug side effects over a large Twitter dataset using Apache Spark. Proceedings of the 9th IEEE\/ACM Int. Conf. on Advances in Social Networks Analysis and Mining (ASONAM), Sidney, Australia, July 2017","DOI":"10.1145\/3110025.3110110"},{"key":"159_CR13","doi-asserted-by":"crossref","unstructured":"Hu M, Liu B (2004) Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, August 2004, pp 168\u2013177","DOI":"10.1145\/1014052.1014073"},{"key":"159_CR14","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1007\/978-3-642-53914-5_37","volume-title":"Advanced data mining and applications","author":"K Jiang","year":"2013","unstructured":"Jiang K, Zheng Y (2013) Mining Twitter data for potential drug effects. In: Motoda H et al (eds) Advanced data mining and applications. Springer, Berlin, pp 434\u2013443"},{"key":"159_CR15","unstructured":"Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent twitter sentiment classification. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, June 2011, pp 151\u2013160"},{"key":"159_CR16","unstructured":"Liu KL, Li WJ, Guo M (2012) Emoticon smoothed language models for Twitter sentiment analysis. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, Ontario, Canada, July 2012, pp 1678\u20131684"},{"key":"159_CR17","unstructured":"Loria S (2015) TextBlob: Simplified Text Processing. http:\/\/textblob.readthedocs.org\/en\/dev\/ Accessed 21 Mar 2015"},{"key":"159_CR18","unstructured":"MetaMap (2013) Semantic Group File. https:\/\/metamap.nlm.nih.gov\/Docs\/SemGroups_2013.txt"},{"key":"159_CR19","unstructured":"MetaMap (2015) Semantic Types and Groups. https:\/\/metamap.nlm.nih.gov\/SemanticTypesAndGroups.shtml"},{"key":"159_CR20","unstructured":"Mohammad SM, Kiritchenko S, Zhu X (2013) NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. Proceedings of the seventh International Workshop on Semantic Evaluation Exercises (SemEval-2013), Atlanta, GA, June 2013"},{"key":"159_CR21","unstructured":"MongoDB, Inc. (2015) PyMongo 3.0 Documentation http:\/\/api.mongodb.org\/python\/current\/ Accessed March 21, 2015"},{"key":"159_CR22","unstructured":"MongoDB, Inc. (2015) Data Modeling Introduction. http:\/\/docs.mongodb.org\/manual\/core\/data-modeling-introduction\/?_ga=1.170474338.315992412.1419057306 Accessed March 21, 2015"},{"key":"159_CR23","unstructured":"NLTK Project (2015) NLTK (Nature Language Toolkit). http:\/\/www.nltk.org\/ . Accessed March 21, 2015"},{"key":"159_CR24","unstructured":"Patientslikeme (2015) https:\/\/www.patientslikeme.com\/ . Accessed March 27, 2015"},{"key":"159_CR25","doi-asserted-by":"crossref","unstructured":"Peng Y., Moh M., Moh T.-S. (2016) Efficient adverse drug event extraction using Twitter sentiment analysis. Proceedings of the 8th IEEE\/ACM Int. Conf. on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, August 2016, pp. 1101-1018","DOI":"10.1109\/ASONAM.2016.7752365"},{"key":"159_CR26","unstructured":"Python Software Foundation (2013) Distance 0.1.3. https:\/\/pypi.python.org\/pypi\/Distance\/ . Accessed March 15, 2015"},{"key":"159_CR27","unstructured":"Sebastiani F et al (2010) SentiWordNet. http:\/\/sentiwordnet.isti.cnr.it\/ . Accessed 25 Mar 2015"},{"key":"159_CR28","unstructured":"The Apache Software Foundation (2014) Apache Hive TM. https:\/\/hive.apache.org\/ . Accessed 15 Mar 2015"},{"key":"159_CR29","unstructured":"The Apache Software Foundation (2016) Apache Spark. http:\/\/spark.apache.org\/"},{"key":"159_CR30","unstructured":"The R Foundation (2015) The R Project for Statistical Computing. https:\/\/www.r-project.org\/ . Accessed 15 Mar 2015"},{"key":"159_CR31","doi-asserted-by":"crossref","unstructured":"Torunoglu D, Telseren G, Sagturk O, Ganiz M (2013) Wikipedia based semantic smoothing for twitter sentiment classification. IEEE Int. Symposium on Innovations in Intelligent Systems and Applications (INISTA), Albena, Bulgaria, June 2013, pp 1\u20135","DOI":"10.1109\/INISTA.2013.6577649"},{"key":"159_CR32","unstructured":"Tweepy (2015) An easy-to-use Python library for accessing the Twitter API. https:\/\/www.tweepy.org\/ . Accessed 15 Mar 2015"},{"key":"159_CR33","unstructured":"U.S. Department of Health and Human Services, Office of Disease Prevention and Health Promotion (2014) National action plan for adverse drug event prevention. https:\/\/health.gov\/hcq\/ade-action-plan.asp . Accessed 21 Mar 2015"},{"key":"159_CR34","unstructured":"U.S. National Library of Medicine (2015) MedlinePlus. https:\/\/www.nlm.nih.gov . Accessed 27 Mar 2015"},{"issue":"2\u20133","key":"159_CR35","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s10579-005-7880-9","volume":"39","author":"J Wiebe","year":"2005","unstructured":"Wiebe J, Wilson T, Cardie C (2005) Annotating expressions of opinions and emotions in language. Lang Resour Evaluat 39(2\u20133):165\u2013210","journal-title":"Lang Resour Evaluat"},{"key":"159_CR36","volume-title":"Data mining: practical machine learning tools and techniques","author":"IH Witten","year":"2016","unstructured":"Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques, 4th edn. Morgan Kaufmann, Cambridge","edition":"4"},{"key":"159_CR37","doi-asserted-by":"crossref","unstructured":"Wu L, Moh T-S, Khuri N (2015) Twitter opinion mining for adverse drug reactions. IEEE International Conference on Big Data, Santa Clara, CA, October 2015, pp 1570\u20131574","DOI":"10.1109\/BigData.2015.7363922"},{"key":"159_CR38","doi-asserted-by":"crossref","unstructured":"Yu F, Moh M, Moh T-S (2016) Towards extracting drug-effect relation from Twitter: a supervised learning approach. IEEE International Conference On Intelligent Data and Security, New York, NY, April 2016, pp 339\u2013344","DOI":"10.1109\/BigDataSecurity-HPSC-IDS.2016.53"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13721-017-0159-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-017-0159-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-017-0159-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,3]],"date-time":"2019-10-03T11:40:25Z","timestamp":1570102825000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13721-017-0159-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,18]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["159"],"URL":"https:\/\/doi.org\/10.1007\/s13721-017-0159-4","relation":{},"ISSN":["2192-6662","2192-6670"],"issn-type":[{"type":"print","value":"2192-6662"},{"type":"electronic","value":"2192-6670"}],"subject":[],"published":{"date-parts":[[2017,9,18]]},"article-number":"18"}}