Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets

Journal of Biomedical Semantics

Volumn 8, Issue 1, 2017, Pages

  Du, Jingcheng ^a   Xu, Jun ^a   Song, Hsingyi ^a   Liu, Xiangyu ^b   Tao, Cui ^a  

^a UNIVERSITY OF TEXAS   (United States)

^b UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Gold standard; Hierarchical classification; Sentiment analysis; Social media; Support vector machines; Twitter

Indexed keywords

WART VIRUS VACCINE;

ATTITUDE TO HEALTH; DATA MINING; EMOTION; HUMAN; MACHINE LEARNING; SOCIAL MEDIA;

ATTITUDE TO HEALTH; DATA MINING; EMOTIONS; HUMANS; MACHINE LEARNING; PAPILLOMAVIRUS VACCINES; SOCIAL MEDIA;

EID: 85014363510     PISSN: None     EISSN: 20411480     Source Type: Journal    
DOI: 10.1186/s13326-017-0120-6     Document Type: Article

References (23)

1. Centers for Disease Control and Prevention. HPV-Associated Cancers Statistics [Internet]. Available from: http://www.cdc.gov/cancer/hpv/statistics/index.htm. Accessed July 2016.
2. Farmar AL, Love-Osborne K, Chichester K, Breslin K, Bronkan K, Hambidge SJ. Achieving High Adolescent HPV Vaccination Coverage. Pediatrics. 2016;5:e20152653.
3. Twitter Usage Statistics [Internet]. Available from: http://www.internetlivestats.com/twitter-statistics/. Accessed Feb 2017.
4. Thakkar H, Patel D. Approaches for sentiment analysis on twitter: A state-of-art study. arXiv preprint arXiv:1512.01043. Accessed 3 Dec 2015.
5. Pak A, Paroubek P. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. InLREc 2010;10(2010).
6. Surian D, Nguyen DQ, Kennedy G, Johnson M, Coiera E, Dunn AG. Characterizing twitter discussions about HPV vaccines using topic modeling and community detection. J Med Internet Res. 2016;18:e232. JMIR Publications Inc.
7. Salathé M, Khandelwal S. Assessing vaccination sentiments with online social media: Implications for infectious disease dynamics and control. PLoS Comput Biol. 2011;7:e1002199.
8. Dunn AG, Leask J, Zhou X, Mandl KD, Coiera E. Associations between exposure to and expression of negative opinions about human papillomavirus vaccines on social media: an observational study. J Med Internet Res. 2015;17(6).
9. Zhou X, Coiera E, Tsafnat G, Arachi D, Ong MS, Dunn AG. Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter. Stud Health Technol Inform. 2015;216:761-5.
10. API Overview [Internet]. Available from: https://dev.twitter.com/overview/api. Accessed Feb 2017.
11. Kester LM, Zimet GD, Fortenberry JD, Kahn JA, Shew ML. A national study of HPV vaccination of adolescent girls: rates, predictors, and reasons for non-vaccination. Matern Child Health J Springer. 2013;17:879-85.
12. Zimet GD, Weiss TW, Rosenthal SL, Good MB, Vichnin MD. Reasons for non-vaccination against HPV and future vaccination intentions among 19-26 year-old women. BMC Womens Health. 2010;10:27. BioMed Central Ltd.
13. Holman DM, Benard V, Roland KB, Watson M, Liddon N, Stokley S. Barriers to human papillomavirus vaccination among US adolescents: a systematic review of the literature. JAMA Pediatr. 2014;168:76-82. American Medical Association.
14. Why Some Parents Are Refusing HPV Vaccine For Their Children [Internet]. Available from: https://shotofprevention.com/2013/08/20/why-some-parents-are-refusing-hpv-vaccine-for-their-children/. Accessed Aug 2013.
15. Stenetorp P, Pyysalo S, Topić G, Ohta T, Ananiadou S, Tsujii JI. BRAT: a web-based tool for NLP-assisted text annotation. InProceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics. 2012;23:102-7.
16. Bhowmick PK, Mitra P, Basu A. An agreement measure for determining inter-annotator reliability of human judgements on affective text. InProceedings of the Workshop on Human Judgements in Computational Linguistics. Association for Computational Linguistics. 2008;23:58-65.
17. Go A, Bhayani R, Huang L. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford. 2009;1(12).
18. Ghazi D, Inkpen D, Szpakowicz S. Hierarchical versus flat classification of emotions in text. InProceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text. Association for Computational Linguistics. 2010;5:140-6.
19. Hsu C-W, Chang C-C, Lin C-J, others. A practical guide to support vector classification. 2003
20. Turian J, Ratinov L, Bengio Y. Word representations: a simple and general method for semi-supervised learning. InProceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics. 2010;11:384-94.
21. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD Explor. 2009;11:10-8. Newsl. ACM.
22. Xu J, Zhang Y, Wu Y, Wang J, Dong X, Xu H. Citation sentiment analysis in clinical trial papers. InAMIA Annual Symposium Proceedings. American Medical Informatics Association. 2015;2015:1334.
23. sklearn.svm. SVR [Internet]. Available from: http://scikitlearn.org/stable/modules/generated/sklearn.svm.SVR.html. February 2017.