Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | Research

Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language

Authors: Azita Yazdani, Mohammad Shamloo, Mina Khaki, Azin Nahvijou

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Login to get access

Abstract

Purpose

Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This paper introduces a model for detecting cancer patients' opinions about healthcare services in the Persian language, both positive and negative.

Method

To achieve the objectives of this study, a combination of sentiment analysis (SA) and topic modeling approaches was employed. All pertinent comments made by cancer patients were collected from the patient feedback form of the Tehran University of Medical Science (TUMS) Cancer Institute (CI) in Iran, from March to October 2021. Conventional evaluation metrics such as accuracy, precision, recall, and F-measure were utilized to assess the performance of the proposed model.

Result

The experimental findings revealed that the proposed SA model achieved accuracies of 89.3%, 92.6%, and 90.8% in detecting patients' sentiments towards general services, healthcare services, and life expectancy, respectively. Based on the topic modeling results, the topic "Metastasis" exhibited lower sentiment scores compared to other topics. Additionally, cancer patients expressed dissatisfaction with the current appointment booking service, while topics such as "Good experience," "Affable staff", and "Chemotherapy" garnered higher sentiment scores.

Conclusion

The combined use of SA and topic modeling offers valuable insights into healthcare services. Policymakers can utilize the knowledge obtained from these topics and associated sentiments to enhance patient satisfaction with cancer institution services.
Appendix
Available only for authorised users
Literature
1.
Yue L, Chen W, Li X, Zuo W, Yin M. A survey of sentiment analysis in social media. Knowl Inf Syst. 2019;60(2):617–63.CrossRef
2.
Jindal K, Aron R. A systematic study of sentiment analysis for social media data. Materials today: proceedings. 2021 .
3.
Campbell L, Evans Y, Pumper M, Moreno MA. Social media use by physicians: a qualitative study of the new frontier of medicine. BMC Med Inform Decis Mak. 2016;16(1):1–11.CrossRef
4.
Khanbhai M, Anyadi P, Symons J, Flott K, Darzi A, Mayer E. Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review. BMJ Health Care Inform. 2021;28(1):e100262.
5.
Abualigah L, Alfar HE, Shehab M, Hussein AMA. Sentiment analysis in healthcare: a brief review. Recent advances in NLP: the case of Arabic language. 2020; 129–41.
6.
Alonso MA, Vilares D, Gómez-Rodríguez C, Vilares J. Sentiment analysis for fake news detection. Electronics. 2021;10(11):1348.CrossRef
7.
Chong WY, Selvaretnam B, Soon L-K, editors. Natural language processing for sentiment analysis: an exploratory analysis on tweets. 2014 4th international conference on artificial intelligence with applications in engineering and technology. IEEE; 2014.
8.
Kanakaraj M, Guddeti RMR, editors. NLP based sentiment analysis on Twitter data using ensemble classifiers. 2015 3Rd international conference on signal processing, communication and networking (ICSCN). IEEE; 2015.
9.
Abirami AM, Askarunisa A. Sentiment analysis model to emphasize the impact of online reviews in healthcare industry. Online Information Review. 2017.
10.
Greaves F, Ramirez-Cano D, Millett C, Darzi A, Donaldson L. Use of sentiment analysis for capturing patient experience from free-text comments posted online. J Med Internet Res. 2013;15(11): e2721.CrossRef
11.
Lai ST, Mafas R, editors. Sentiment Analysis in Healthcare: Motives, Challenges & Opportunities pertaining to Machine Learning. 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE; 2022.
12.
Clark EM, James T, Jones CA, Alapati A, Ukandu P, Danforth CM, et al. A sentiment analysis of breast cancer treatment experiences and healthcare perceptions across twitter. arXiv preprint arXiv:180509959. 2018.
13.
Ahmad S, Manzoor S, Siddiqui S, Mariappan N, Zafar I, Ahmad A, et al., editors. Epigenetic underpinnings of inflammation: Connecting the dots between pulmonary diseases, lung cancer and COVID-19. Seminars in cancer biology. Elsevier; 2022.
14.
Han CJ, Yang GS, Syrjala K. Symptom experiences in colorectal cancer survivors after cancer treatments: A systematic review and meta-analysis. Cancer Nurs. 2020;43(3):E132.CrossRefPubMedPubMedCentral
15.
Rodrigues RG, das Dores RM, Camilo-Junior CG, Rosa TC. SentiHealth-Cancer: a sentiment analysis tool to help detecting mood of patients in online social networks. Int J Med Inform. 2016;85(1):80–95.
16.
Adikari A, De Silva D, Ranasinghe WK, Bandaragoda T, Alahakoon O, Persad R, et al. Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories. PLoS ONE. 2020;15(3): e0229361.CrossRefPubMedPubMedCentral
17.
Dashtipour K, Poria S, Hussain A, Cambria E, Hawalah AY, Gelbukh A, et al. Multilingual sentiment analysis: state of the art and independent comparison of techniques. Cogn Comput. 2016;8:757–71.CrossRef
18.
Alexander G, Bahja M, Butt GF. Automating Large-scale Health Care Service Feedback Analysis: Sentiment Analysis and Topic Modeling Study. JMIR Med Inform. 2022;10(4): e29385.CrossRefPubMedPubMedCentral
19.
Edara DC, Vanukuri LP, Sistla V, Kolli VKK. Sentiment analysis and text categorization of cancer medical records with LSTM. J Ambient Intell Humaniz Comput. 2023;14(5):5309–25.CrossRef
20.
Rajabi Z, Valavi M. A survey on sentiment analysis in Persian: a comprehensive system perspective covering challenges and advances in resources and methods. Cogn Comput. 2021;13(4):882–902.CrossRef
21.
Balakrishnan A, Idicula SM, Jones J. Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience. Health Informatics J. 2021;27(2):14604582211007536.CrossRefPubMed
22.
Modave F, Zhao Y, Krieger J, He Z, Guo Y, Huo J, et al. Understanding perceptions and attitudes in breast cancer discussions on Twitter. Studies in health technology and informatics. 2019;264:1293.PubMedPubMedCentral
23.
Edara DC, Vanukuri LP, Sistla V, Kolli VKK. Sentiment analysis and text categorization of cancer medical records with LSTM. J Ambient Intell Hum Comput. 2019;10(7):1–17.
24.
Qiu B, Zhao K, Mitra P, Wu D, Caragea C, Yen J, et al., editors. Get online support, feel better--sentiment analysis and dynamics in an online cancer survivor community. 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing. IEEE; 2011.
25.
Vehviläinen-Julkunen K, Turpeinen S, Kvist T, Ryden-Kortelainen M, Nelimarkka S, Enshaeifar S, et al. Experience of ambulatory cancer care: understanding patients’ perspectives of quality using sentiment analysis. Cancer Nurs. 2021;44(6):E331–8.CrossRefPubMed
26.
Dalayya S, Elsaid STFA, Ng KH, Song TL, Lim JBY. Sentiment Analysis to Understand the Perception and Requirements of a Plant-Based Food App for Cancer Patients. Hum Behav Emerg Technol. 2023;2023:1–11.
27.
Ofek N, Caragea C, Rokach L, Biyani P, Mitra P, Yen J, et al., editors. Improving sentiment analysis in an online cancer survivor community using dynamic sentiment lexicon. 2013 international conference on social intelligence and technology. IEEE; 2013.
28.
Baker MR, Mohammed EZ, Jihad KH, editors. Prediction of Colon Cancer Related Tweets Using Deep Learning Models. International Conference on Intelligent Systems Design and Applications. Springer; 2022.
29.
Alamoodi A, Baker MR, Albahri O, Zaidan B, Zaidan A, Wong W-K, et al. Public sentiment analysis and topic modeling regarding COVID-19’s three waves of total lockdown: a case study on movement control order in Malaysia. KSII Trans Internet Inf Syst. 2022;16(7):2169–90.
30.
Pandesenda AI, Yana RR, Sukma EA, Yahya A, Widharto P, Hidayanto AN, editors. Sentiment Analysis of Service Quality of Online Healthcare Platform Using Fast Large-Margin. 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE; 2020.
31.
Alayba AM, Palade V, England M, Iqbal R, editors. Arabic language sentiment analysis on health services. 2017 1st international workshop on arabic script analysis and recognition (asar). IEEE; 2017.
32.
del Arco FMP, Valdivia MTM, Zafra SMJ, González MDM, Cámara EM. COPOS: corpus of patient opinions in Spanish. Application of sentiment analysis techniques. Procesamiento del Lenguaje Natural. 2016;57:83–90.
33.
Freedman RA, Viswanath K, Vaz-Luis I, Keating NL. Learning from social media: utilizing advanced data extraction techniques to understand barriers to breast cancer treatment. Breast Cancer Res Treat. 2016;158:395–405.CrossRefPubMedPubMedCentral
34.
Khaleghparast S, Maleki M, Hajianfar G, Soumari E, Oveisi M, Golandouz HM, et al. Development of a patients’ satisfaction analysis system using machine learning and lexicon-based methods. BMC Health Serv Res. 2023;23(1):1–12.CrossRef
35.
Acharjee UK, Arefin M, Hossen KM, Uddin MN, Uddin MA, Islam L. Sequence-to-sequence learning-based conversion of pseudo-code to source code using neural translation approach. IEEE Access. 2022;10:26730–42.CrossRef
36.
37.
Rezaee R, Asadi S, Yazdani A, Rezvani A, Kazeroon AM. Development, usability and quality evaluation of the resilient mobile application for women with breast cancer. Health Science Reports. 2022;5(4): e708.CrossRefPubMedPubMedCentral
38.
Shamsfard M. Challenges and open problems in Persian text processing. Proc LTC. 2011;11:65–9.
39.
Kasmaiee S, Kasmaiee S, Homayounpour M. Correcting spelling mistakes in Persian texts with rules and deep learning methods. Scientific Reports. 2023;13(1):19945.
40.
Dolamic L, Savoy J. When stopword lists make the difference. J Am Soc Inform Sci Technol. 2010;61(1):200–3.CrossRef
41.
Dolamic L, Savoy J, editors. Persian language, is stemming efficient? 2009 20th International Workshop on Database and Expert Systems Application. IEEE; 2009.
42.
43.
Basiri ME, Naghsh-Nilchi AR, Ghasem-Aghaee N. Sentiment prediction based on dempster-shafer theory of evidence. Mathematical Problems in Engineering. 2014; 2014.
44.
Thelwall M, Buckley K, Paltoglou G. Sentiment strength detection for the social web. J Am Soc Inform Sci Technol. 2012;63(1):163–73.CrossRef
45.
Calvo RA, Mac KS. Emotions in text: dimensional and categorical models. Comput Intell. 2013;29(3):527–43.CrossRef
46.
Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003;3(Jan):993–1022.
47.
Jebamani B, Murugeswari R, Nagaraj P. Online Appointment Management System in Hospitals Using Distributed Resource Allocation Algorithm. Sentimental Analysis and Deep Learning: Springer; 2022. p. 291–301.
48.
Vidnes TK, Wahl AK, Andersen MH. Patient experiences following liver transplantation due to liver metastases from colorectal cancer. Eur J Oncol Nurs. 2013;17(3):269–74.
49.
Mazza M, Piperis M, Aasaithambi S, Chauhan J, Sagkriotis A, Vieira C. Social Media Listening to Understand the Lived Experience of Individuals in Europe With Metastatic Breast Cancer: A Systematic Search and Content Analysis Study. Front Oncol. 2022;12.
50.
Zhang S, Bantum E, Owen J, Elhadad N, editors. Does sustained participation in an online health community affect sentiment? AMIA Annual Symposium Proceedings; 2014: American Medical Informatics Association.
51.
Shirzadi SM, Sheibani-Tehrani D, Shirzadi NS, Yaghoubi M. Identifying factors affecting hoteling quality in Iranian public hospitals using DEMATEL approach. J Educ Health Promot. 2020;9.
52.
Alemi F, Torii M, Clementz L, Aron DC. Feasibility of real-time satisfaction surveys through automated analysis of patients' unstructured comments and sentiments. Qual Manag Health Care. 2012;21(1):9–19.
53.
Shams M, Shakery A, Faili H, editors. A non-parametric LDA-based induction method for sentiment analysis. The 16th CSI international symposium on artificial intelligence and signal processing (AISP 2012). Shiraz, Iran: IEEE; 2012.
54.
Bagheri A, Saraee M, de Jong F, editors. Sentiment classification in Persian: Introducing a mutual information-based method for feature selection. 2013 21st Iranian conference on electrical engineering (ICEE). IEEE; 2013.
55.
Saraee M, Bagheri A, editors. Feature selection methods in Persian sentiment analysis. Natural Language Processing and Information Systems: 18th International Conference on Applications of Natural Language to Information Systems, NLDB 2013, Salford, UK, June 19–21, 2013 Proceedings 18. Springer; 2013.
Metadata
Title
Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language
Authors
Azita Yazdani
Mohammad Shamloo
Mina Khaki
Azin Nahvijou
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02358-2

Other articles of this Issue 1/2023

BMC Medical Informatics and Decision Making 1/2023 Go to the issue

Research

Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms

Research

Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques

Research

Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization

Research

MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning

Research

Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study

Research

Research on outpatient capacity planning combining lean thinking and integer linear programming