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Medical Social Media and Its Characteristics

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Sentiment Analysis in the Medical Domain
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Abstract

Medical social media derivatives such as tweets, online forums or drug reviews, can be subject of interest for medical sentiment analysis. Such data is data published by individuals (not necessarily patients, but their relatives, friends and healthcare professionals). While tweets are restricted in their length and are therefore characterised by a specific style of writing which is very concise and full with abbreviations, data from online communities or review sites can be more comprehensive. This chapter describes the various social media text types that have already been used for medical sentiment analysis along with their linguistic and semantic peculiarities.

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Notes

  1. 1.

    https://developer.twitter.com/en/docs/twitter-api.

  2. 2.

    http://www.wedmd.com.

  3. 3.

    https://www.reddit.com/r/cancer/.

  4. 4.

    http://www.medhelp.org/.

References

  1. Bahja, M., Lycett, M.: Identifying patient experience from online resources via sentiment analysis and topic modelling. In: Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 94–99 (2016). https://doi.org/10.1145/3006299.3006335

  2. Boonitt, S., Skunkan, Y.: Public perception of the covid-19 pandemic on twitter: Sentiment analysis and topic modeling study. JMIR Public Health Surveill. 6, e21978 (2020)

    Article  Google Scholar 

  3. Colón-Ruiz, C., Segura-Bedmar, I.: Comparing deep learning architectures for sentiment analysis on drug reviews. J. Biomed. Inf. 110, 103539 (2020)

    Article  Google Scholar 

  4. Craig, W., Boniel-Nissim, M., King, N., Walsh, S.D., Boer, M., Donnelly, P.D., Harel-Fisch, Y., Malinowska-Cieślik, M., de Matos, M.G., Cosma, A., et al.: Social media use and cyber-bullying: a cross-national analysis of young people in 42 countries. J. Adolesc. Health 66(6), S100–S108 (2020)

    Article  Google Scholar 

  5. Denecke, K.: Health Web Science: Social Media Data for Healthcare. Springer (2015)

    Google Scholar 

  6. Denecke, K., Nejdl, W.: How valuable is medical social media data? Content analysis of the medical web. Inf. Sci. 179(12), 1870–1880 (2009)

    Google Scholar 

  7. Foufi, V., Timakum, T., Gaudet-Blavignac, C., Lovis, C., Song, M., et al.: Mining of textual health information from reddit: analysis of chronic diseases with extracted entities and their relations. J. Med. Internet Res. 21(6), e12876 (2019)

    Article  Google Scholar 

  8. Gräßer, F., Kallumadi, S., Malberg, H., Zaunseder, S.: Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning. In: Proceedings of the 2018 International Conference on Digital Health, pp. 121–125 (2018). https://doi.org/10.1145/3194658.3194677

  9. Liu, J., Zhang, W., Jiang, X., Zhou, Y.: Data mining of the reviews from online private doctors. Telemedicine e-Health 26(9), 1157–1166 (2020)

    Article  Google Scholar 

  10. O’dea, B., Larsen, M.E., Batterham, P.J., Calear, A.L., Christensen, H.: A linguistic analysis of suicide-related twitter posts. Crisis 38(5), 319 (2017)

    Article  Google Scholar 

  11. of Michigan, U.: Kaggle. UMICH SI650 - sentiment classification (2018). https://www.kaggle.com/c/si650winter11

  12. Pandesenda, A.I., Yana, R.R., Sukma, E.A., Yahya, A.N., Widharto, P., Hidayanto, A.N.: Sentiment analysis of service quality of online healthcare platform using fast large-margin. In: 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), pp. 121–125 (2020)

    Google Scholar 

  13. Smith, P., Lee, M.: Cross-discourse development of supervised sentiment analysis in the clinical domain. In: Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, pp. 79–83 (2012)

    Google Scholar 

  14. Wang, Y., McKee, M., Torbica, A., Stuckler, D.: Systematic literature review on the spread of health-related misinformation on social media. Soc. Sci. Med. 240, 112552 (2019)

    Article  Google Scholar 

  15. Yoo, M., Jang, C.W.: Physical rehabilitation on social media during covid-19: topics and sentiments analysis of tweets. Ann. Phys. Rehab. Med. 65, 101589–101589 (2021)

    Article  Google Scholar 

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Denecke, K. (2023). Medical Social Media and Its Characteristics. In: Sentiment Analysis in the Medical Domain. Springer, Cham. https://doi.org/10.1007/978-3-031-30187-2_3

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  • DOI: https://doi.org/10.1007/978-3-031-30187-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30186-5

  • Online ISBN: 978-3-031-30187-2

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