Understanding what patients think about hospitals: A deep learning approach for detecting emotions in patient opinions

Serrano-Guerrero, Jesús; Bani-Doumi, Mohammad; Romero, Francisco P.; Olivas, José Á.

doi:10.1016/j.artmed.2022.102298

Cited by 11 publications

(2 citation statements)

References 45 publications

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“…FastText functions by exploiting subword information and considers the internal structure of words instead of learning word representations. FastText divides words into n-grams rather than using individual words and learns vectors for subparts of words, which are so-called characters of n-grams [18]. The FastText scenario model used pre-trained word vectors for 157 languages.…”

Section: Word Embedding Modelmentioning

confidence: 99%

A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19

Purwitasari¹,

Ansyah²,

Kurniawan³

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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As a result of the COVID-19 pandemic, there have been restrictions on activities outside the home which has caused people to interact more and express their emotions through social media platforms, one of which is Twitter. Previous studies on emotion classification used only one feature extraction, namely the lexicon based or word embedding. Feature extraction using the emotion lexicon has the advantage of recognizing emotional words in a sentence while feature extraction using word embedding has the advantage of recognizing the semantic meaning. Therefore, the main contribution to this research is to use two lexicon feature extraction and word embedding to classify emotions. The classification technique used in this research is the Ensemble Voting Classifier by selecting the two best classifiers to try on both types of feature extraction. The experimental results for both types of feature extraction are the same, indicating that the best classifiers are Random Forest and SVM. Models using both types of feature extraction show increased accuracy compared to using only one feature extraction. The results of this emotional analysis can be used to determine the public's reaction to an event, product, or public policy.

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Section: Word Embedding Modelmentioning

confidence: 99%

A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19

Purwitasari¹,

Ansyah²,

Kurniawan³

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

show abstract

“…K et al [23] applied deep learning to reader emotion research, verified the effectiveness and interpretability of deep learning in this research field through the Bi-LSTM attention model, and found that reader emotion may be related to specific words and named entities. Serrano-Guerrero et al [24] constructed a deep learning architecture of bi-directional gated recurrent units with multichannel convolutional neural network layers to detect the emotions of patient reviewers and understand their attitudes towards hospitals. This compensated for the inability of hospital assessment systems to detect patient emotions and provided directions for future research.…”

Section: Deep Learning In the Emotion Domainmentioning

confidence: 99%

Changes in Public Sentiment under the Background of Major Emergencies—Taking the Shanghai Epidemic as an Example

Zhang

Lin

Luo

et al. 2022

IJERPH

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The occurrence of major health events can have a significant impact on public mood and mental health. In this study, we selected Shanghai during the 2019 novel coronavirus pandemic as a case study and Weibo texts as the data source. The ERNIE pre-training model was used to classify the text data into five emotional categories: gratitude, confidence, sadness, anger, and no emotion. The changes in public sentiment and potential influencing factors were analyzed with the emotional sequence diagram method. We also examined the causal relationship between the epidemic and public sentiment, as well as positive and negative emotions. The study found: (1) public sentiment during the epidemic was primarily affected by public behavior, government behavior, and the severity of the epidemic. (2) From the perspective of time series changes, the changes in public emotions during the epidemic were divided into emotional fermentation, emotional climax, and emotional chaos periods. (3) There was a clear causal relationship between the epidemic and the changes in public emotions, and the impact on negative emotions was greater than that of positive emotions. Additionally, positive emotions had a certain inhibitory effect on negative emotions.

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An integrated decision framework for evaluating and recommending health care services

Alshouha,

Serrano-Guerrero,

Chiclana

et al. 2024

Appl Intell

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Quality management techniques such as the quality function deployment model can help hospitals assess and improve the quality of their services by integrating the voice of customers. The different quality parameters of this model are usually determined and assessed by experts; nonetheless, obtaining such experts is not always easy or inexpensive. Moreover, in this method, patient opinions are not usually considered directly, although they are the real users of the services and those who can best assess those services. Nevertheless, these opinions are easily accessible today, owing to the development of medical social networks where patients directly convey their opinions about the different services and features of a hospital. Therefore, it is feasible to replace expert knowledge with the information provided by these opinions. Based on this idea, this study proposes a novel fuzzy recommendation model based on the quality function deployment method to rank hospitals depending on patient opinions and preferences regarding hospital services. This model integrates a topic modeling strategy for determining hospital requirements, customer needs, and the relationship between them as well as a sentiment analysis algorithm for assessing customer satisfaction regarding hospital services. To demonstrate the usefulness of the proposed method, several experiments were conducted using patient reviews from real hospitals, and the method was compared against other recommendation models. The results prove that this approach represents a step toward more personalized and effective health care system selection considering patient preferences and opinions.

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Understanding what patients think about hospitals: A deep learning approach for detecting emotions in patient opinions

Cited by 11 publications

References 45 publications

A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19

A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19

Changes in Public Sentiment under the Background of Major Emergencies—Taking the Shanghai Epidemic as an Example

An integrated decision framework for evaluating and recommending health care services

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