Levels and Tasks of Sentiment Analysis
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-30187-2_8
Reference5 articles.
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. de Las Heras-Pedrosa, C., Sánchez-Núñez, P., Peláez, J.I.: Sentiment analysis and emotion understanding during the covid-19 pandemic in Spain and its impact on digital ecosystems. Int. J. Environ. Res. Public Health 17(15), 5542 (2020)
3. 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
4. Nazir, A., Rao, Y., Wu, L., Sun, L.: Issues and challenges of aspect-based sentiment analysis: a comprehensive survey. IEEE Trans. Affect. Comput. 13(2), 845–863 (2022)
5. Sun, Q., Tang, T.Y.: On the computational study of Chinese Alzheimer’s disease online communities: a sentiment and contextual analysis approach. In: Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence, pp. 104–108 (2018). https://doi.org/10.1145/3243250.3243259
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