Patient's Feedback Platform for Quality of Services via “Free Text Analysis” in Healthcare Industry
Abstract
Data analysis of social media posting continues to offer a huge variety of information about the health situation faced by an individual. Social networking or social media websites provide us a wealth of information generated by users in a variety of domains, that generated information are unstructured and unlabeled and are not captured in an exceedingly systematic manner, as info generated is not humanly possible to process due to its size. One traditional way of collecting patients experience is by conducting surveys and questionnaires, as these methods ask fixed questions and are expensive to administer. In this paper, a patient feedback platform (PFP) using free text sentiment analysis is developed to computationally identify and categorize the polarity expressed in a piece of text. Six machine learning latest algorithms have been used as key evaluation for evaluating accuracy of the developed (PFP) model. Results achieved have shown 88 % accuracy on the basis of which it is recommended that developed (PFP) patient feedback platform could be used to improve E-health care services indeed.
Downloads
References
F. Greaves, D. Ramirez-Cano, C. Millett, A. Darzi, and L. Donaldson, Use of sentiment analysis for capturing patient experience from free-text comments posted online, J. Med. Internet Res., vol. 15, no. 11, Nov. 2013, doi: 10.2196/jmir.2721. DOI: https://doi.org/10.2196/jmir.2721
J. Bollen, H. Mao, and X.-J. Zeng, Twitter mood predicts the stock market, J. Comput. Sci., vol. 2, no. 1, pp. 1–8, 2011, doi: 10.1016/j.jocs.2010.12.007. DOI: https://doi.org/10.1016/j.jocs.2010.12.007
M. Walther and M. Kaisser, Geo-spatial event detection in the Twitter stream, in Advances in Information Retrieval, 2013, pp. 356–367, doi: 10.1007/978-3-642-36973-5_30. DOI: https://doi.org/10.1007/978-3-642-36973-5_30
D. Corney, C. Martin, and A. Göker, Spot the ball: Detecting sports events on twitter, in European Conference on Information Retrieval, 2014, vol. 8416 LNCS, pp. 449–454, doi: 10.1007/978-3-319-06028-6_40. DOI: https://doi.org/10.1007/978-3-319-06028-6_40
M. Madhukar and S. Verma, Hybrid Semantic Analysis of Tweets: A Case Study of Tweets on Girl-Child in India, Eng. Technol. Appl. Sci. Res., vol. 7, no. 5, pp. 2014–2016, 2017.
B. C. Wallace, M. J. Paul, U. Sarkar, T. A. Trikalinos, and M. Dredze, A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews, J. Am. Med. Inform. Assoc., vol. 21, no. 6, pp. 1098–1103, 2014, doi: 10.1136/amiajnl-2014-002711. DOI: https://doi.org/10.1136/amiajnl-2014-002711
O. Ivanciuc, Weka machine learning for predicting the phospholipidosis inducing potential, Curr. Top. Med. Chem., vol. 8, no. 18, pp. 1691–709, 2008, doi: 10.2174/156802608786786589. DOI: https://doi.org/10.2174/156802608786786589
B. Liu, Sentiment analysis and opinion mining, Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–184, 2012, doi: 10.2200/S00416ED1V01Y201204HLT016. DOI: https://doi.org/10.2200/S00416ED1V01Y201204HLT016
S. Khatoon, Real-time Twitter Data Analysis of Saudi Telecom Companies for Enhanced Customer Relationship Management, Int. J. Comput. Sci. Netw. Secur., vol. 17, no. 2, pp. 141–147, 2017.
P. Smith, Sentiment Analysis Of Patient Feedback, Doctoral dissertation, University of Birmingham, 2017.
Parul Pandey, Simplifying Sentiment Analysis using VADER in Python (on Social Media Text). https://www.datacamp.com/ community/news/simplifying-sentiment-analysis-using-vader-in-python-on-social-media-zoewd3c40d (accessed Feb. 02, 2020).
Opinion-Mining-using-the-UCI-Drug-Review-Dataset/drug, review processed.csv.gz at master · WuraolaOyewusi/Opinion-Mining-using-the-UCI-Drug-Review-Dataset · GitHub. https://github.com/WuraolaOyewusi/Opinion-Mining-using-the-UCI-Drug-Review-Dataset/blob/master/drug review processed.csv.gz (accessed Feb. 02, 2020).
A. Bin Raies, H. Mansour, R. Incitti, and V. B. Bajic, Combining Position Weight Matrices and Document-Term Matrix for Efficient Extraction of Associations of Methylated Genes and Diseases from Free Text, PLoS ONE, vol. 8, no. 10, p. e77848, Oct. 2013, doi: 10.1371/journal.pone.0077848. DOI: https://doi.org/10.1371/journal.pone.0077848
Copyright (c) 2020 EMITTER International Journal of Engineering Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright to this article is transferred to Politeknik Elektronika Negeri Surabaya(PENS) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to PENS. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here .
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
- Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
- Authors may reproduce or authorize others to reproduce the work or derivative works for the author’s personal use or company use, provided that the source and the copyright notice of Politeknik Elektronika Negeri Surabaya (PENS) publisher are indicated.
- Authors are allowed to use and reuse their articles under the same CC-BY-NC-SA license as third parties.
- Third-parties are allowed to share and adapt the publication work for all non-commercial purposes and if they remix, transform, or build upon the material, they must distribute under the same license as the original.
Plagiarism Check
To avoid plagiarism activities, the manuscript will be checked twice by the Editorial Board of the EMITTER International Journal of Engineering Technology (EMITTER Journal) using iThenticate Plagiarism Checker and the CrossCheck plagiarism screening service. The similarity score of a manuscript has should be less than 25%. The manuscript that plagiarizes another author’s work or author's own will be rejected by EMITTER Journal.
Authors are expected to comply with EMITTER Journal's plagiarism rules by downloading and signing the plagiarism declaration form here and resubmitting the form, along with the copyright transfer form via online submission.
