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. 2023 Nov 29;23(1):275.
doi: 10.1186/s12911-023-02358-2.

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

Azita Yazdani  1   2   3 , Mohammad Shamloo  4 , Mina Khaki  4 , Azin Nahvijou  5
Affiliations

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

Azita Yazdani et al. BMC Med Inform Decis Mak. .

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.

Keywords: Cancer; Natural language processing; Opinion mining; Patient feedback; Sentiment analysis; Topic modeling.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The block diagram of the research methodology
Fig. 2
Fig. 2
Pseudo code for the proposed model
Fig. 3
Fig. 3
Age-frequency of patients participating in the free-text feedback form
Fig. 4
Fig. 4
The frequency of categories of patients' comments based on the five levels of satisfaction
Fig. 5
Fig. 5
Persian words cloud of patients' comments about the healthcare services
Fig. 6
Fig. 6
Relationship between topic frequency and sentiment
See this image and copyright information in PMC

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