@InProceedings{robin-EtAl:2020:LREC,
  author    = {Robin, Cécile  and  Isazad Mashinchi, Mona  and  Ahmadi Zeleti, Fatemeh  and  Ojo, Adegboyega  and  Buitelaar, Paul},
  title     = {A Term Extraction Approach to Survey Analysis in Health Care},
  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month          = {May},
  year           = {2020},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {2069--2077},
  abstract  = {The voice of the customer has for a long time been a key focus of businesses in all domains. It has received a lot of attention from the research community in Natural Language Processing (NLP) resulting in many approaches to analyzing customers feedback ((aspect-based) sentiment analysis, topic modeling, etc.). In the health domain, public and private bodies are increasingly prioritizing patient engagement for assessing the quality of the service given at each stage of the care. Patient and customer satisfaction analysis relate in many ways. In the domain of health particularly, a more precise and insightful analysis is needed to help practitioners locate potential issues and plan actions accordingly. We introduce here an approach to patient experience with the analysis of free text questions from the 2017 Irish National Inpatient Survey campaign using term extraction as a means to highlight important and insightful subject matters raised by patients. We evaluate the results by mapping them to a manually constructed framework following the Activity, Resource, Context (ARC) methodology (Ordenes, 2014) and specific to the health care environment, and compare our results against manual annotations done on the full 2017 dataset based on those categories.},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.254}
}