Lexicon-Based Medical Sentiment Analysis
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-30187-2_10
Reference5 articles.
1. Asghar, M.Z., Ahmad, S., Qasim, M., Zahra, S.R., Kundi, F.M.: Sentihealth: creating health-related sentiment lexicon using hybrid approach. SpringerPlus 5, 1 (2016)
2. 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
3. Deng, Y., Declerck, T., Lendvai, P., Denecke, K.: The generation of a corpus for clinical sentiment analysis. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) The Semantic Web, pp. 311–324. Springer International Publishing, Cham (2016)
4. Holderness, E., Cawkwell, P., Bolton, K., Pustejovsky, J., Hall, M.H.: Distinguishing clinical sentiment: The importance of domain adaptation in psychiatric patient health records. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 117–123. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/W19-1915. https://aclanthology.org/W19-1915
5. Liu, S., Lee, I.: Extracting features with medical sentiment lexicon and position encoding for drug reviews. Health Inf. Sci. Syst. 7(1), 1–10 (2019)
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