Abstract
Every day a large amount of subjective information is generated through social networks such as Facebook® and Twitter®. The subjective information implies the opinions, beliefs, feelings and attitudes that people express towards different topics of interest. Moreover, this type of information is of great importance for companies, organizations or individuals, because it allows them to carry out actions that benefit them. Besides, sentiment analysis is the field that studies subjective information through natural language processing, computational linguistics, information retrieval and data mining techniques. Sentiment analysis is very useful in various domains, such as politics, marketing, tourism, among others. Actually, healthcare domain implies a large area of opportunity to obtain benefits using sentiment analysis, such as obtaining information about the patients’ mood, diseases, adverse drug reactions, epidemics, among others. However, healthcare domain has been very little explored. Therefore, in this chapter we propose a module based on sentiment analysis to obtain sentiments and emotions at the comment and entity levels from texts related to the healthcare domain. Also, different case studies are presented to validate the proposed module.
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References
Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowl. Syst. 89, 14–46 (2015). https://doi.org/10.1016/j.knosys.2015.06.015
Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., Herrera-Viedma, E.: Sentiment analysis: a review and comparative analysis of web services. Inf. Sci. (Ny) 311, 18–38 (2015). https://doi.org/10.1016/j.ins.2015.03.040
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5, 1093–1113 (2014). https://doi.org/10.1016/j.asej.2014.04.011
Fernández-Gavilanes, M., Álvarez-López, T., Juncal-Martínez, J., Costa-Montenegro, E., Javier González-Castaño, F.: Unsupervised method for sentiment analysis in online texts. Expert Syst. Appl. 58, 57–75 (2016). https://doi.org/10.1016/j.eswa.2016.03.031
Bucur, C.: Using opinion mining techniques in tourism. Proc. Econ. Financ. 23, 1666–1673 (2015). https://doi.org/10.1016/S2212-5671(15)00471-2
Gull, R., Shoaib, U., Rasheed, S., Abid, W., Zahoor, B.: Pre processing of twitter’s data for opinion mining in political context. Proc. Comput. Sci. 96, 1560–1570 (2016). https://doi.org/10.1016/j.procs.2016.08.203
Crannell, W.C., Clark, E., Jones, C., James, T.A., Moore, J.: A pattern-matched twitter analysis of US cancer-patient sentiments. J. Surg. Res. 206, 536–542 (2018). https://doi.org/10.1016/j.jss.2016.06.050
Rodrigues, R.G., das Dores, R.M., Camilo-Junior, C.G., Rosa, T.C.: SentiHealth-Cancer: a sentiment analysis tool to help detecting mood of patients in online social networks. Int. J. Med. Inform. 85, 80–95 (2016). https://doi.org/10.1016/j.ijmedinf.2015.09.007
Bui, N., Yen, J., Honavar, V.: Temporal causality analysis of sentiment change in a cancer survivor network. IEEE Trans. Comput. Soc. Syst. 3, 75–87 (2016). https://doi.org/10.1109/TCSS.2016.2591880
Korkontzelos, I., Nikfarjam, A., Shardlow, M., Sarker, A., Ananiadou, S., Gonzalez, G.H.: Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts. J. Biomed. Inform. 62, 148–158 (2016). https://doi.org/10.1016/j.jbi.2016.06.007
Wu, L., Moh, T.S., Khuri, N.: Twitter opinion mining for adverse drug reactions. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 1570–1574 (2015)
Gopalakrishnan, V., Ramaswamy, C.: Patient opinion mining to analyze drugs satisfaction using supervised learning. J. Appl. Res. Technol. 15, 311–319 (2017). https://doi.org/10.1016/j.jart.2017.02.005
Asghar, M.Z., Ahmad, S., Qasim, M., Zahra, S.R., Kundi, F.M.: SentiHealth: creating health-related sentiment lexicon using hybrid approach. Springerplus 5, 1139 (2016). https://doi.org/10.1186/s40064-016-2809-x
Du, J., Xu, J., Song, H.-Y., Tao, C.: Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data. BMC Med. Inform. Decis. Mak. 17, 69 (2017). https://doi.org/10.1186/s12911-017-0469-6
Zhou, X., Coiera, E.W., Tsafnat, G., Arachi, D., Ong, M.-S., Dunn, A.G.: Using social connection information to improve opinion mining: identifying negative sentiment about HPV vaccines on twitter. Stud. Health Technol. Inform. 216, 761–765 (2015)
Birjali, M., Beni-Hssane, A., Erritali, M.: Machine learning and semantic sentiment analysis based algorithms for suicide sentiment prediction in social networks. Proc. Comput. Sci. 113, 65–72 (2017). https://doi.org/10.1016/j.procs.2017.08.290
Sabra, S., Malik, K.M., Alobaidi, M.: Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives. Comput. Biol. Med. 94, 1–10 (2018). https://doi.org/10.1016/j.compbiomed.2017.12.026
Salas-Zárate, M. del P., Medina-Moreira, J., Lagos-Ortíz, K., Luna-Aveiga, H., Rodríguez-García, M.Á., Valencia-García, R.: Sentiment analysis on tweets about diabetes: an aspect-level approach. Comput. Math. Methods Med. 9 (2017). https://doi.org/10.1155/2017/5140631
Ji, X., Chun, S.A., Geller, J.: Monitoring public health concerns using twitter sentiment classifications. In: 2013 IEEE International Conference on Healthcare Informatics, pp. 335–344 (2013)
Yang, F.-C., Lee, A.J.T., Kuo, S.-C.: Mining health social media with sentiment analysis. J. Med. Syst. 40, 236 (2016). https://doi.org/10.1007/s10916-016-0604-4
Izzo, J.A., Maloy, K.: 86 sentiment analysis demonstrates variability in medical student grading. Ann. Emerg. Med. 70, S35–S36 (2017). https://doi.org/10.1016/j.annemergmed.2017.07.111
Alayba, A.M., Palade, V., England, M., Iqbal, R.: Arabic language sentiment analysis on health services. CoRR. abs/1702.0 (2017)
Facebook: Graph API. https://developers.facebook.com/docs/graph-api
Williams, A.: TwitterOAuth. https://twitteroauth.com/
IBM: Natural language understanding. https://www.ibm.com/watson/services/natural-language-understanding/
Plotly: Plotly.js, https://plot.ly/javascript/
Acknowledgements
The authors are grateful to the National Technological Institute of Mexico for supporting this work. This research paper was also supported by the Mexico’s National Council of Science and Technology (CONACYT), as well as by the Secretariat of Public Education (SEP) through the PRODEP program.
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Ramírez-Tinoco, F.J., Alor-Hernández, G., Sánchez-Cervantes, J.L., Salas-Zárate, M.d., Valencia-García, R. (2019). Use of Sentiment Analysis Techniques in Healthcare Domain. In: Alor-Hernández, G., Sánchez-Cervantes, J., Rodríguez-González, A., Valencia-García, R. (eds) Current Trends in Semantic Web Technologies: Theory and Practice. Studies in Computational Intelligence, vol 815. Springer, Cham. https://doi.org/10.1007/978-3-030-06149-4_8
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