PREDICTING RATINGS FOR USER REVIEWS AND OPINION MINING ANALYZE FOR PHYSICIANS AND HOSPITALS
Health care is taking its turn in the internet now and online health information consumption is also booming. Users have started generating healthcare
reports like online doctor reviews open to all. Hence, online health forums are increasingly popular these days since people can gather their required
data by just sitting at home and select the best doctor by considering the reviews available online. The patients also browse on their concerned
diseases and use the open forum for discussion on the topics. On an average, these online health-care providers are mainly focusing on reviews about
the physicians. The feedback provided by patients is considered and we also analyze the sentiments of the patient to estimate the value of the reviews.
The rating for the doctors is divided into various categories such as Staff, Knowledge, and Helpfulness. We propose support vector machine and apriori
for the classification of data and use sentiment based rating prediction to analyze doctor's reviews and opinion mining patterns for online patterns.
By providing physician ratings in website, it offers the patients to know about the physician and consider the critique and information to make their
Keywords: Support vector machine, Apriori, Sentiment classification, Opinion mining.
1. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in Vector Space. arXiv:1301.3781v3 [cs. CL] 7 Sep 2013.
2. Zhang X, Cun YL. Text Understanding from Scratch. arXiv:1502.01710v5[cs. LG] 4 Apr 2016.
3. Paul MJ, Wallace BC, Dredze M. What Affects Patient (Dis) satisfaction? Analyzing Online Doctor Ratings with a Joint Topic-Sentiment Model. Copyright Â©2013.
4. Monett D, Stolte H. Predicting star ratings based on annotated reviews of mobile Apps. dx.doi.org/10.15439/2016F141 24 October 2016.
5. Galizzi MM, Miraldo MM, Stavropoulou C, Desai M, Jayatunga W, Joshi M, Parikh S. Adadelta: An adaptive learning rate method. arXiv:1212.5701v1 [cs. LG] 22 Dec 2012.
6. Zeiler MD. Deep sparse rectifier neural networks. Copyright 2011 by the authors.
7. Rama T. Siamese Convolutional Networks for Cognate Identification. 24 October 2016.arXiv:1605.05172v2 [cs.CL].
8. Turian J, Ratinov L, Bengio Y. Word representations: A simple and general method for semi-supervised learning. 11-16 July 2010. Â©2010.
9. Pennington J, Socher R, Manning CD. GloVe: Global vectors for word representation. October 25-29, 2014, Doha, Qatar. Â© 2014.
10. Kim Y. Convolutional Neural Networks for Sentence Classification. October 25-29, 2014, Doha, Qatar. Â© 2014.
11. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuglu K, Kuksa P. Natural Language Processing (Almost) from Scratch. J Mach Learn Res 2011;12:2493-537.
12. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from over fitting. J Mach Learn Res 2014;15(1):1929-58.
13. Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modeling sentences. arXiv preprint arXiv:1404.2188, 2014.
14. Zhang X, LeCun Y. Text understanding from scratch. arXiv preprint arXiv: 1502.01710, 2015.
15. Turian J, Ratinov L, Bengio Y. Word representations: A simple and general method for semi-supervised learning. In: Proceeding the 48th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, July 2010. p. 384-94.
16. Ahire SB, Khanuja HK. A personalized framework for healthcare recommendation. Int J Comput Appl 2015;110(1):89-92.
The publication is licensed under CC By and is open access. Copyright is with author and allowed to retain publishing rights without restrictions.