@inproceedings{,
    author = {Kersting, Joschka; Geierhos, Michaela},
    title = {Well-Being in Plastic Surgery: Deep Learning Reveals Patients' Evaluations},
    editor = {Quix, Christoph; Hammoudi, Slimane; van der Aalst, Wil},
    booktitle = {Proceedings of the 10th International Conference on Data Science, Technology and Applications (DATA 2021)},
    series = {},
    journal = {},
    address = {},
    publisher = {SCITEPRESS},
    edition = {},
    year = {2021},
    isbn = {978-989-758-521-0},
    volume = {},
    number =  {},
    pages = {275-284},
    url = {},
    doi = {10.5220/0010576002750284},
    keywords = {Aspect-based Sentiment Analysis ; Information Extraction ; Deep Learning ; Transformer Applications},
    abstract = {This study deals with aspect-based sentiment analysis, the correlation of extracted aspects and their sentiment polarities with metadata. There are millions of review texts on the Internet that cannot be analyzed and thus people cannot benefit from the contained information. While most research so far has focused on explicit aspects from product or service data (e.g., hotels), we extract and classify implicit and explicit aspect phrases from German-language physician review texts. We annotated aspect phrases that indicate ratings about the doctor’s practice, such as waiting time or general perceived well-being conveyed by all staff members of a practice. We also apply a sentiment polarity classifier. While we compare several traditional and transformer networks, we apply the best model, the multilingual XLM-RoBERTa, to a dedicated German-language dataset dealing with plastic surgeons. We choose plastic surgery as sample domain because it is especially sensitive with its relation to a person’s self-image and felt acceptance. In addition to standard evaluation measures such as Precision, Recall, and F1-Score, we correlate our results with metadata from physician review websites, such as a physician’s gender. We figure out several correlations and present methods for analyzing unstructured review texts to enable service improvements in healthcare.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Informatik, INF 7 - Institut für Datensicherheit, Professur: Geierhos, Michaela},
    
    
}