Analysis of patient satisfaction in Dutch and Spanish online reviews

  1. Maks, Isa
  2. Izquierdo Beviá, Rubén
  3. Jiménez Zafra, Salud M.
  4. Martín Valdivia, María Teresa
Zeitschrift:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Datum der Publikation: 2017

Nummer: 58

Seiten: 101-108

Art: Artikel

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Andere Publikationen in: Procesamiento del lenguaje natural

Zusammenfassung

El Análisis de Sentimientos es una tarea del Procesamiento del Lenguaje Natural que ha sido estudiada en diferentes dominios como el de películas, teléfonos móviles u hoteles. Sin embargo, otras áreas como el dominio médico no han sido exploradas todavía. En este trabajo presentamos un corpus de opiniones de pacientes formado por una parte en holandés (COPOD: Corpus of Patient Opinions in Dutch) y por otra parte en español (COPOS: Corpus of Patient Opinions in Spanish). Además, se han realizado diferentes experimentos en ambas lenguas utilizando un método supervisado (SVM), una aproximación basada en cross-domain y un método basado en diccionario. Los resultados obtenidos superan el método base en casi todos los casos e incluso los resultados de otros clasificadores de polaridad en el dominio del paciente. Con respecto al bilingüismo, los sistemas desarrollados para holandés y español proporcionan resultados similares para las medidas F1 y Accuracy.

Bibliographische Referenzen

  • Biyani, P., C. Caragea, P. Mitra, C. Zhou, J. Yen, G. E. Greer, and K. Portier. 2013. Co-training over domain-independent and domain-dependent features for sentiment analysis of an online cancer support community. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2013, pages 413–417. IEEE.
  • Bobicev, V., M. Sokolova, Y. Jafer, and
  • D. Schramm. 2012. Learning sentiments from tweets with personal health information. In Canadian Conference on Artificial Intelligence, pages 37–48. Springer.
  • Chang, C.-C. and C.-J. Lin. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27.
  • Denecke, K. and Y. Deng. 2015. Sentiment analysis in medical settings: New opportunities and challenges. Artificial intelligence in medicine, 64(1):17–27.
  • Fox, S. and M. Duggan. 2013. Health online 2013. Health, pages 1–55.
  • Greaves, F., D. Ramirez-Cano, C. Millett, A. Darzi, and L. Donaldson. 2013. Use of sentiment analysis for capturing patient experience from free-text comments posted online. Journal of medical Internet research, 15(11):e239.
  • Liu, B. 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1):1–167.
  • Maks, I., R. Izquierdo, F. Frontini, R. Agerri, and P. Vossen. 2014. Generating Polarity Lexicons with Wordnet propagation in five languages. In Proceedings of LREC2014, Reykjavik.
  • Maks, I. and P. Vossen. 2013. Sentiment Analysis of Reviews: Should we analyze writer intentions or reader perceptions? In Proceedings of RANLP 2003, pages 415– 419, Hissar, Bulgaria.
  • Melzi, S., A. Abdaoui, J. Azé, S. Bringay, P. Poncelet, and F. Galtier. 2014. Patient’s rationale: Patient Knowledge retrieval from health forums. In eTELEMED: eHealth, Telemedicine, and Social Medicine.
  • Molina-González, M. D., E. Martínez-Cámara, M.-T. Martín-Valdivia, and J. M. PereaOrtega. 2013. Semantic orientation for polarity classification in spanish reviews. Expert Systems with Applications, 40(18):7250–7257.
  • Na, J.-C., W. Y. M. Kyaing, C. S. Khoo, S. Foo, Y.-K. Chang, and Y.-L. Theng. 2012. Sentiment classification of drug reviews using a rule-based linguistic approach. In International Conference on Asian Digital Libraries, pages 189–198. Springer.
  • Ofek, N., C. Caragea, L. Rokach, P. Biyani, P. Mitra, J. Yen, K. Portier, and G. Greer. 2013. Improving sentiment analysis in an online cancer survivor community using dynamic sentiment lexicon. In Social Intelligence and Technology (SOCIETY), 2013 International Conference on, pages 109– 113. IEEE.
  • Plaza-del Arco, F. M., M. T. Martín-Valdivia, S. M. Jiménez-Zafra, M. D. MolinaGonzález, and E. Martínez-Cámara. 2016. COPOS: Corpus Of Patient Opinions in Spanish. Application of Sentiment Analysis Techniques. Procesamiento del Lenguaje Natural, 57:83–90.
  • Qiu, B., K. Zhao, P. Mitra, D. Wu, C. Caragea, J. Yen, G. E. Greer, and K. Portier. 2011. Get online support, feel better–sentiment analysis and dynamics in an online cancer survivor community. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on, pages 274–281. IEEE.
  • Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, March.
  • Sharif, H., F. Zaffar, A. Abbasi, and D. Zimbra. 2014. Detecting adverse drug reactions using a sentiment classification framework.
  • Van de Belt, T. H., L. J. Engelen, S. A. Berben, S. Teerenstra, M. Samsom, and L. Schoonhoven. 2013. Internet and social media for health-related information and communication in health care: preferences of the Dutch general population. Journal of medical Internet research, 15(10):e220.
  • Vapnik, V. 2013. The nature of statistical learning theory. Springer Science & Business Media.
Fuente de los datos: Dialnet