Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online

Abstract

BackgroundThere are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care.ObjectiveWe attempted to use machine learning to understand patients’ unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient’s own quantitative rating of their care.MethodsWe applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England.ResultsThere was 81%, 84%, and 89% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40–.74, P<.001 for all). We observed mild to moderate associations between our machine learning predictions and responses to the large patient survey for the three categories examined (Spearman rho 0.37-0.51, P<.001 for all).ConclusionsThe prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free-text, a reasonably accurate assessment of patients’ opinion about different performance aspects of a hospital and that these machine learning predictions are associated with results of more conventional surveys.

A review of Sentiment Analysis in Spanish

“…For this, they use social networks, like Twitter and Facebook as sources to review written texts in form of comments, which contain opinions about their registered brand. This literature review found different types of works framed within different applications; in tourism [10] [11], movie reviews [12] [13], sports [14] politics [15] [16] education [17], health [18], finance [19], and opinion reviews on automobiles [20].…”

Collaborative Filtering for Estimating Health Related Utilities in Decision Support Systems

“…Each thread t steps: (i) QoL dimension identification and (ii) Degree former is intended to identify those threads where at le ns considered in the EuroQol questionnaire (mobility, s /discomfort, anxiety/depression) is addressed. The latte means of sentiment analysis techniques [6], the degree ach of the identified QoL dimensions. These two inf ow to virtually score the corresponding EuroQol questi he conversion to a proper UC using the standard EuroQ gorithm.…”

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“…Studies pertaining to the patient view have mostly relied on questionnaire-based surveys that employ structured measurements of patient reported outcomes [10][11][12]. Perhaps, a better way to understand the patient view would be exploring an alternative source of data.…”

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