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

• Published in: BMC medical informatics and decision making Vol. 23; no. 1; p. 275
• Main Authors: Yazdani, Azita, Shamloo, Mohammad, Khaki, Mina, Nahvijou, Azin
• Format: Journal Article
• Language: English
• Published: England BioMed Central 29-11-2023; BMC
• Subjects: Artificial intelligence; Attitude; Breast cancer; Cancer; Cancer therapies; Chemotherapy; Customer feedback; Data mining; Datasets; Emotions; English language; Health care; Health services; Hospitals; Humans; Internet; Iran; Language; Life expectancy; Life span; Machine learning; Medical science; Metastases; Model accuracy; Modelling; Natural language processing; Neoplasms - therapy; Neural networks; Opinion mining; Patient feedback; Patients; Performance assessment; Persian language; Quality assessment; Sentiment Analysis; Social networks; Topic modeling; Iran; Topic modeling; Sentiment analysis; Natural language processing; Opinion mining; Patient feedback; Cancer
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-023-02358-2

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.
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.
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.
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.