Open Access
Issue
E3S Web Conf.
Volume 319, 2021
International Congress on Health Vigilance (VIGISAN 2021)
Article Number 01064
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/202131901064
Published online 09 November 2021
  1. Wiebe J, Bruce R. Probabilistic classifiers for tracking point of view. Progress in communication sciences 1995:125-142 [FREE Full text] [Google Scholar]
  2. Hatzivassiloglou V, McKeown KR. Predicting the Semantic Orientation of Adjectives. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics. 1997 Presented at: ACL’98/EACL’98; July 7-12, 1997; Madrid, Spain p. 174-181 URL: https://www.aclweb.org/anthology/P97-1023/ [CrossRef] [Google Scholar]
  3. Wiebe, JM, Bruce, RF, O’Hara TP. Development and Use of a Gold-standard Data Set for Subjectivity Classifications. In: Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics. 1999 Presented at: ACL’99; June 20-26, 1999; College Park, Maryland, USA p. 246-253 URL: https://www.aclweb.org/anthology/P99-1032/ [CrossRef] [Google Scholar]
  4. Hu, M, Liu B. Mining Opinion Features in Customer Reviews. In: Proceedings of the 19th national conference on Artifical intelligence. 2004 Presented at: AAAI’04; July 25 - 29, 2004; San Jose, California, USA p. 755-760 URL: https://dl.acm.org/citation.cfm?id=1597269 [Google Scholar]
  5. Hu, M, Liu B. Mining and Summarizing Customer Reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 2004 Presented at: KDD’04; August 22 - 25, 2004; Seattle, Washington, USA p. 168-177 URL: https://dl.acm.org/citation.cfm?id=1014073 [CrossRef] [Google Scholar]
  6. Bollen, J, Mao, H, Zeng X. Twitter mood predicts the stock market. J Comput Sci 2011; 2(1):1-8. [CrossRef] [Google Scholar]
  7. Efron M. Cultural orientation: Classifying subjective documents by cociation analysis. In: Proceedings of the AAAI Fall Symposium on Style and Meaning in Language, Art, and Music. 2004 Presented at: AAAI’04; July 25-29, 2004; San Jose, California p. 41-48. [Google Scholar]
  8. Ramteke, J, Shah, S, Godhia, D, Shaikh A. Election Result Prediction Using Twitter Sentiment Analysis. In: Proceedings of the 2016 International Conference on Inventive Computation Technologies. 2016 Presented at: ICICT’16; August 26-27, 2016; Coimbatore, India p. 1-5. [CrossRef] [Google Scholar]
  9. World Health Organisation. Geneva, Switzerland: World Health Organisation; 2006. Constitution of the World Health Organisation URL: https://www.who.int/governance/eb/who_constitution_en.pdf [accessed 2019-11-12] [Google Scholar]
  10. Huber, M, Knottnerus, JA, Green, L, van der Horst, H, Jadad, AR, Kromhout, D, et al. How should we define health? Br Med J 2011 Jul 26;343:d4163. [CrossRef] [Medline] [Google Scholar]
  11. Berg O. Health and quality of life. Acta Sociologica 1975;18(1):3-22. [CrossRef] [Google Scholar]
  12. Afyouni, S, Fetit, AE, Arvanitis TN. #DigitalHealth: exploring users’ perspectives through social media analysis. Stud Health Technol Inform 2015;213:243-246. [Medline] [Google Scholar]
  13. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res 2009 Mar 27;11(1):e11 [FREE Full text] [CrossRef] [Medline] [Google Scholar]
  14. Eysenbach G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am J Prev Med 2011 May;40(5 Suppl 2):S154-S158. [CrossRef] [Medline] [Google Scholar]
  15. Rozenblum, R, Bates D. Patient-centred healthcare, social media and the internet: the perfect storm? BMJ Qual Saf 2013 Feb 01;22(3):183-186. [CrossRef] [Google Scholar]
  16. Ofcom. 2015. The communications market report URL: http://stakeholders.ofcom.org.uk/binaries/research/cmr/cmr15/icmr15/icmr_2015.pdf [WebCite Cache] [Google Scholar]
  17. Lunden I. Techcrunch. 2013. Mobile twitterm+ (75%) access from handheld devices monthly, 65% of ad sales come from mobile URL: http://techcrunch.com/2013/10/03/mobile-twitter-161m-access-from-handheld-devices-each-month-65-of-ad-revenues-coming-from-mobile/ [accessed 2016-04-13] [WebCite Cache] [Google Scholar]
  18. Pang, B, Lee L. Opinion mining and sentiment analysis. Foundations and trends in information retrieval 2008 Jul;2(1-2):1-35. [Google Scholar]
  19. Liu, B, Zhang L. A survey of opinion mining and sentiment analysis. Mining Text Data 2012:415-463. [CrossRef] [Google Scholar]
  20. Nasukawa T. Sentiment analysis: capturing favorability using natural language processing. 2003 Jan 01 Presented at: Proceedings of the 2nd International Conference on Knowledge; October 23-25, 2003; Sanibel Island, FL, USA. [CrossRef] [Google Scholar]
  21. Chew, C, Eysenbach G. Pandemics in the age of twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One 2010 Nov 29;5(11):e14118 [FREE Full text] [CrossRef] [Medline] [Google Scholar]
  22. Mohammad S. 9 – Sentiment analysis: detecting valence, emotions, and other affectual states from text. Emotion Measurement 2016:201-237. [CrossRef] [Google Scholar]
  23. Hu, M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’04. New York, NY, USA: ACM; 2004. p. 168–77 [Google Scholar]
  24. Mishne G. Experiments with mood classification in blog posts. In: 1st Workshop on stylistic analysis of text for information access. 2005. [Google Scholar]
  25. Baldoni, M, Baroglio, C, Patti, V, Rena P. From tags to emotions: ontology-driven sentiment analysis in the social semantic web. Intell Artif 2012; 6(1):41–54 [Google Scholar]
  26. Pang, B, Lee, L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing – Volume 10, EMNLP ’02. Stroudsburg, PA, USA: Association for Computational Linguistics; 2002. p. 79–86. [Google Scholar]
  27. Pang, B, Lee L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics, ACL ’04. Stroudsburg, PA, USA: Association for Computational Linguistics; 2004 [Google Scholar]
  28. Turney PD. Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics, ACL ’02. Stroudsburg, PA, USA: Association for Computational Linguistics; 2002. p. 417–24 [Google Scholar]
  29. Pang, B, Lee, L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing – Volume 10, EMNLP ’02. Stroudsburg, PA, USA: Association for Computational Linguistics; 2002. p. 79–86 [Google Scholar]
  30. Pontiki, M, Galanis, D, Pavlopoulos, J, Papageorgiou, H, Androutsopoulos, I, Manandhar S. Semeval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014). August 2014. p. 27–35 [Google Scholar]
  31. Denecke K. Are sentiwordnet scores suited for multi-domain sentiment classification? Proceedings of the fourth international conference on digital information management, 2009. ICDIM 2009. 2009. p. 1–6 [Google Scholar]
  32. Montejo-Ráez, A, Martínez-Cámara, E, Martín-Valdivia, MT, López Laurena. ˜ Random walk weighting over sentiwordnet for sentiment polarity detection on twitter. In: Proceedings of the 3rd workshop in computational approaches to subjectivity and sentiment analysis, WASSA ’12. Stroudsburg, PA, USA: Association for Computational Linguistics; 2012. p. 3–10. [Google Scholar]
  33. Baccianella, S, Esuli, A, Sebastiani F. Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari N (Conference Chair), Choukri, K, Maegaard, B, Mariani, J, Odijk, J, Piperidis, S, Rosner, M, Tapias, D, editors. Proceedings of the seventh international conference on language resources and evaluation (LREC’10). Valletta, Malta: European Language Resources Association (ELRA); 2010. [Google Scholar]
  34. Balahur, A, Steinberger, R, Kabadjov, MA, Zavarella, V, Van der Goot, E, Halkia, M, et al. Sentiment analysis in the news. Comput Res Repos (CoRR) 2013, abs/1309.6202 [Google Scholar]
  35. Wilson, T, Wiebe, J, Hoffmann P. Recognizing contextual polarity in phraselevel sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing, HLT ’05. Stroudsburg, PA, USA: Association for Computational Linguistics; 2005. p. 347–54 [Google Scholar]
  36. Mohammad, SM, Turney PD. Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, CAAGET ’10. Stroudsburg, PA, USA: Association for Computational Linguistics; 2010. p. 26–34. [Google Scholar]
  37. Deng, L, Choi, Y, Wiebe J. Benefactive/malefactive event and writer attitude annotation. In: Proceedings of the 51st annual meeting of the association for computational linguistics (Volume 2: Short Papers). Sofia, Bulgaria: Association for Computational Linguistics; August 2013. p. 120–5 [Google Scholar]
  38. Goeuriot, L, Na J-C, Min Kyaing, WY, Khoo, C, Chang Y-K, Theng Y-L, et al. Sentiment lexicons for health-related opinion mining. In: Proceedings of the 2Nd ACM SIGHIT international health informatics symposium, IHI ’12. New York, NY, USA: ACM; 2012. p. 219–26. [Google Scholar]
  39. Ohana, B, Tierney, B, Delany S. Domain independent sentiment classification with many lexicons. In: 2011 IEEE workshops of international conference on advanced information networking and applications (WAINA). March 2011. p. 632–7 [Google Scholar]
  40. A. Mecke, I. Lee, J.R. Baker jr., M.M. Banaszak Holl, B.G. Orr, Eur. Phys. J. E 14, 7 (2004) [Google Scholar]
  41. M. Ben Rabha, M.F. Boujmil, M. Saadoun, B. Bessaïs, Eur. Phys. J. Appl. Phys. (to be published) [Google Scholar]
  42. L. T. De Luca, Propulsion physics (EDP Sciences, Les Ulis, 2009) [Google Scholar]
  43. F. De Lillo, F. Cecconi, G. Lacorata, A. Vulpiani, EPL, 84 (2008) [Google Scholar]
  44. G. Plancque, D. You, E. Blanchard, V. Mertens, C. Lamouroux, Role of chemistry in the phenomena occurring in nuclear power plants circuits, in Proceedings of the International Congress on Advances in Nuclear power Plants, ICAPP, 2-5 May 2011, Nice, France (2011) [Google Scholar]
  45. K. Denecke, Y. Deng, Sentiment analysis in medical settings: New opportunities and challenges, Artificial Intelligence in Medicine, march 2015. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.