Use of Sentiment Analysis Techniques in Healthcare Domain
- 210 Downloads
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.
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.
- 10.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.2016.06.007CrossRefGoogle Scholar
- 11.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)Google Scholar
- 15.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)Google Scholar
- 19.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)Google Scholar
- 22.Alayba, A.M., Palade, V., England, M., Iqbal, R.: Arabic language sentiment analysis on health services. CoRR. abs/1702.0 (2017)Google Scholar
- 23.Facebook: Graph API. https://developers.facebook.com/docs/graph-api
- 24.Williams, A.: TwitterOAuth. https://twitteroauth.com/
- 25.IBM: Natural language understanding. https://www.ibm.com/watson/services/natural-language-understanding/