Sentiment analysis model to emphasize the impact of online reviews in healthcare industry
Abstract
Abstract Purpose The purpose of this paper is to develop a systematic approach to extract users' feelings and emotions about their experiences in hospitals from online reviews and rank the places using multi-criteria decision making (MCDM) techniques based on the aggregated sentiment score. Design/methodology/approach The proposed model used a linguistic approach to extract the sentiment words from the free text. It used term frequency-inverse document frequency values to represent features of various places in bag-of-words format. Sentiment dictionary is used to calculate senti-scores. It used different MCDM techniques like simple additive weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods for ranking hospitals based on their aggregated senti-score. Findings Statistical correlation analysis between the rankings of places reveals that the TOPSIS method is the most suitable ranking technique among other MCDM techniques. By improving the senti-score, one can bring their enterprise to the top position. Research limitations/implications Data set is collected from different websites like Twitter, Facebook, etc., for various services/features. Moderate amount of reviews are collected for each place. But not all users give their views on the social media websites. It would be essential to collect responses from all the customers who avail different services at different places. Practical implications The sentiment analysis model proposed in this paper enables B2C and C2C commerce. Business may take suitable measures to overcome their issues/problems raised by the consumer. Consumers can share and educate other consumers about their experiences. Social implications The development of internet has strong influence in all types of industries like healthcare. The availability of internet has changed the way of accessing the information and sharing their experience with others. This paper recognizes the use and impact of social media on the healthcare industry by analyzing the users' feelings expressed in the form of free text. A suitable decision-making technique is applied to rank the places, which enables the users to plan their treatment place in a better way. Originality/value The paper develops a novel approach by applying the TOPSIS method to rank the different alternative places of the healthcare industry by using the senti-score derived from the users' feelings, emotions and experiences expressed in the form of free text.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many health care providers worry about broadcasting misinformation and its negative influence on patients' health decisions.
“Many health care providers worry about broadcasting misinformation and its negative influence on patients’ health decisions [54,58,59].”
From Improving the Theoretical Understanding Toward Patient-Driven Health Care Innovation Through Online Value Cocreation: Systematic Review (2020) · cites this paper as [59]
Cites
Multiple Attribute Decision Making: Methods and Applications
Chen, S.-J. J., Hwang, C. L., Beckmann, M. J., & Krelle, W. E. (1981) · Medical Entomology and Zoology · Cited by 8,207 · see citing works
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024 · see citing works
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,299 · see citing works
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-02145-9 · Cited by 1,872 · see citing works
What do we know about social media in tourism? A review
Zeng, B., & Gerritsen, R. (2014) · Tourism Management Perspectives · doi:10.1016/j.tmp.2014.01.001 · Cited by 1,171 · see citing works
Opinion Word Expansion and Target Extraction through Double Propagation
Qiu, G., Liu, B., Bu, J., et al. (2011) · Computational Linguistics · doi:10.1162/coli_a_00034 · Cited by 1,102 · see citing works
Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods
Amiri, M. P. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.02.103 · Cited by 440 · see citing works
The 14th World Conference on Earthquake Engineering
Alulema, R. X. P., Quiroz, L. G., Scaletti, H., et al. (2008) · Cited by 437 · see citing works
Expanding domain sentiment lexicon through double propagation
Qiu, G., Liu, B., Bu, J., et al. (2009) · Cited by 415 · see citing works
Büyüközkan, G., & Çifçi, G. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.08.061 · Cited by 395 · see citing works
Integrating AHP and data mining for product recommendation based on customer lifetime value
Liu, D., & Shih, Y. (2004) · Information & Management · doi:10.1016/j.im.2004.01.008 · Cited by 350 · see citing works
Kang, D., & Park, Y. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.07.101 · Cited by 269 · see citing works
The utility of linguistic rules in opinion mining
Ding, X., & Liu, B. (2007) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1277741.1277921 · Cited by 213 · see citing works
Generating positive word‐of‐mouth in the service experience
Ng, S., David, M. E., & Dagger, T. S. (2011) · Managing Service Quality · doi:10.1108/09604521111113438 · Cited by 192 · see citing works
Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis
Meena, A., & Prabhakar, T. V. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-71496-5_53 · Cited by 121 · see citing works
A strategic service quality approach using analytic hierarchy process
Chow, C. C., & Luk, P. (2005) · Managing Service Quality · doi:10.1108/09604520510597827 · Cited by 105 · see citing works
Social media in tourism: a visual analytic approach
Cheng, M., & Edwards, D. (2015) · Current Issues in Tourism · doi:10.1080/13683500.2015.1036009 · Cited by 92 · see citing works
Harb, A., Plantié, M., Dray, G., Mathieu Roche, A., Trousset, F., & Pascal Poncelet, B. (2008) · Conference on Soft Computing as Transdisciplinary Science and Technology · doi:10.1145/1456223.1456269 · Cited by 90 · see citing works
Xin, J. Y., Ramayah, T., Soto‐Acosta, P., Popa, S., & Ping, T. A. (2014) · Information Systems Management · doi:10.1080/10580530.2014.890425 · Cited by 50 · see citing works
Jin, C., Ma, T., Hou, R., et al. (2015) · IETE Journal of Research · doi:10.1080/03772063.2015.1021385 · Cited by 36 · see citing works
Comparing sentiment expression in movie reviews from four online genres
Na, J., Thet, T. T., & Khoo, C. S. G. (2010) · Online Information Review · doi:10.1108/14684521011037016 · Cited by 36 · see citing works
Multi-Criteria Recommender Systems based on Multi-Attribute Decision Making
Hdioud, F., Frikh, B., & Ouhbi, B. (2013) · doi:10.1145/2539150.2539176 · Cited by 34 · see citing works
Semantic Feature Clustering for Sentiment Analysis of English Reviews
Agarwal, B., & Mittal, N. (2014) · IETE Journal of Research · doi:10.1080/03772063.2014.963172 · Cited by 33 · see citing works
Automatic Multi-document Summarization Based on Clustering and Nonnegative Matrix Factorization
Park, S., Cha, B., & An, D.-U. (2010) · IETE Technical Review · doi:10.4103/0256-4602.60169 · Cited by 21 · see citing works
Varanasi, P., & Tanniru, M. R. (2015) · Information Systems Management · doi:10.1080/10580530.2015.1044342 · Cited by 17 · see citing works
Feature-based Product Review Summarization Utilizing User Score
Yang, J.-Y., Kim, H., & Lee, S. (2010) · Cited by 16 · see citing works
A Quantity–Quality Composite Ranking of Indian Institutions in CS Research
Uddin, A., & Singh, V. K. (2015) · IETE Technical Review · doi:10.1080/02564602.2015.1010614 · Cited by 16 · see citing works
IITB-Sentiment-Analysts: Participation in Sentiment Analysis in Twitter SemEval 2013 Task
Chawla, K., Ramteke, A., & Bhattacharyya, P. (2013) · Cited by 15 · see citing works
Using data mining for service satisfaction performance analysis for mainland tourists in Taiwan
Pan, W. T. (2014) · International Journal of Technology Management · doi:10.1504/ijtm.2014.059236 · Cited by 11 · see citing works
Automating Opinion Analysis in Film Reviews: The Case of Statistic Versus Linguistic Approach
Poirier, D., Bothorel, C., Neef, É. G. D., et al. (2011) · Text, speech and language technology · doi:10.1007/978-94-007-1757-2_11 · Cited by 10 · see citing works
Expanding the Boundaries of Your Research Using Social Media: Stand-Up and Be Counted
Kumar, M. J. (2014) · IETE Technical Review · doi:10.1080/02564602.2014.944442 · Cited by 9 · see citing works
Evolution of social media and consumer behaviour changes in tourism destination promotion
Presti, O. L., & Raj, R. (2014) · International Journal of Business and Globalisation · doi:10.1504/ijbg.2014.060217 · Cited by 8 · see citing works
Yazdi, A. K. (2015) · International Journal of Productivity and Quality Management · doi:10.1504/ijpqm.2015.069640 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Rezaei Aghdam, A., Watson, J., Cliff, C., & Miah, S. J. (2020) · Journal of Medical Internet Research · doi:10.2196/16324 · Cited by 57 · see citing works
- Authors
- Abirami, A. M., & Askarunisa, A.
- Affiliations
- Thiagarajar College of Engineering (IN)
- Vickram College of Engineering, Sivagangai, India
- ORCID
- Abirami, A.M.
- Venue
- Online Information Review
- Publisher
- Emerald
- Published
- 2017-08-14
- Volume
- 41
- Issue
- 4
- Pages
- 471-486
- ISSN
- 1468-4527 (ISSN-L)
- OpenAlex
- W2735447265
- Cited by
- 71
- References
- 33
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 12 · 2023: 10 · 2022: 12 · 2021: 9 · 2020: 6 · 2019: 2 · 2018: 5
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Online Information Review (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Health Sciences › Health Professions › General Health Professions › Patient Satisfaction in Healthcare
- Keywords
- sentiment analysis, online reviews, hospital rankings, TOPSIS, multi-criteria decision making, social media, health sector, user experience, sentiment lexicon, TF-IDF, bag-of-words, simple additive weighting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
