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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional Autónoma de México
2017
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| Accès en ligne: | https://www.redalyc.org/articulo.oa?id=47452572001 https://www.redalyc.org/journal/474/47452572001/ https://www.redalyc.org/journal/474/47452572001/html/ https://www.redalyc.org/journal/474/47452572001/47452572001.epub https://www.redalyc.org/journal/474/47452572001/movil |
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Table des matières:
- Patient opinion mining to analyze drugs satisfaction using supervised learning Vinodhini Gopalakrishnan Chandrasekaran Ramaswamy Ingeniería drugs health opinion Sentiment classification Opinion mining is a very challenging problem, since user generated content is described in various complex ways using natural language. Inopinion mining, most of the researchers have worked on general domains such as electronic products, movies, and restaurants reviews but notmuch on health and medical domains. Patients using drugs are often looking for stories from patients like them on the internet which they cannotalways find among their friends and family. Few studies investigating the impact of social media on patients have shown that for some healthproblems, online community support results in a positive effect. The opinion mining method employed in this work focuses on predicting the drugsatisfaction level among the other patients who already experienced the effect of a drug. This work aims to apply neural network based methodsfor opinion mining from social web in health care domain. We have extracted the reviews of two different drugs. Experimental analysis is done toanalyze the performance of classification methods on reviews of two different drugs. The results demonstrate that neural network based opinionmining approach outperforms the support vector machine method in terms of precision, recall and f-score. It is also shown that the performance ofradial basis function neural network method is superior than probabilistic neural network method in terms of the performance measures used. 2017 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47452572001 https://www.redalyc.org/journal/474/47452572001/ https://www.redalyc.org/journal/474/47452572001/html/ https://www.redalyc.org/journal/474/47452572001/47452572001.epub https://www.redalyc.org/journal/474/47452572001/movil en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.4 Vol.15