“Not by Our Feeling, But by Other's Seeing”: Sentiment Analysis Technique in Cardiology—An Exploratory Review

Sentiment Analysis (SA) is a novel branch of Natural Language Processing (NLP) that measures emotions or attitudes behind a written text. First applications of SA in healthcare were the detection of disease-related emotional polarities in social media. Now it is possible to extract more complex attitudes (rank attitudes from 1 to 5, assign appraisal values, apply multiple text classifiers) or feelings through NLP techniques, with clear benefits in cardiology; as emotions were proved to be veritable risk factors for the development of cardiovascular diseases (CVD). Our narrative review aimed to summarize the current directions of SA in cardiology and raise the awareness of cardiologists about the potentiality of this novel domain. This paper introduces the readers to basic concepts surrounding medical SA and the need for SA in cardiovascular healthcare. Our synthesis of the current literature proved SA's clinical potential in CVD. However, many other clinical utilities, such as the assessment of emotional consequences of illness, patient-physician relationship, physician intuitions in CVD are not yet explored. These issues constitute future research directions, along with proposing detailed regulations, popularizing health social media among elders, developing insightful definitions of emotional polarity, and investing research into the development of powerful SA algorithms.

[1]  Bedir Tekinerdogan,et al.  Systematic reviews in sentiment analysis: a tertiary study , 2021, Artificial Intelligence Review.

[2]  J. Stulak,et al.  Computational sentiment analysis of an online left ventricular assist device support forum: positivity predominates. , 2020, Annals of cardiothoracic surgery.

[3]  Frederica Gonçalves,et al.  Designing Positive Behavior Change Experiences: a Systematic Review and Sentiment Analysis based on Online User Reviews of Fitness and Nutrition Mobile Applications , 2020, MUM.

[4]  Andrzej Majkowski,et al.  Eye-Tracking Analysis for Emotion Recognition , 2020, Comput. Intell. Neurosci..

[5]  S. Moore,et al.  Feasibility of an Emotion Regulation Intervention for Patients in Cardiac Rehabilitation , 2020, Western journal of nursing research.

[6]  Mohammed Kaity,et al.  Sentiment lexicons and non-English languages: a survey , 2020, Knowledge and Information Systems.

[7]  12. Impact of sentiment analysis tools to improve patients’ life in critical diseases , 2020, Computational Intelligence for Machine Learning and Healthcare Informatics.

[8]  Ashwin S. Nathan,et al.  Telemedicine Outpatient Cardiovascular Care during the COVID-19 Pandemic: Bridging or Opening the Digital Divide? , 2020, Circulation.

[9]  Aravinda Thiagalingam,et al.  Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text–Based Secondary Prevention Program: Feasibility Study , 2020, JMIR mHealth and uHealth.

[10]  Yang Song,et al.  Is Co-Infection with Influenza Virus a Protective Factor of COVID-19? , 2020 .

[11]  James Mountstephens,et al.  Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges , 2020, Sensors.

[12]  Qin Li,et al.  Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet , 2020, Sensors.

[13]  G. Briganti,et al.  Artificial Intelligence in Medicine: Today and Tomorrow , 2020, Frontiers in Medicine.

[14]  José Luis Sánchez-Cervantes,et al.  A Sentiment Analysis Approach for Drug Reviews in Spanish , 2020, Research on computing science.

[15]  Ram Dushad,et al.  A study of drug attitude and medication adherence and its relationship with the impact of illness among the mentally ill , 2019, Archives of Clinical Psychiatry (São Paulo).

[16]  Yue Wang,et al.  Heart sound signals can be used for emotion recognition , 2019, Scientific Reports.

[17]  A. Krishnaveni,et al.  Sentiment Analysis on Myocardial Infarction Using Tweets Data , 2019, Asian Journal of Computer Science and Technology.

[18]  Varun Sapra,et al.  Semantic Analysis of Cardiovascular Disease Sentiment in Online Social Media , 2019, Social Science Research Network.

[19]  B. Manjula,et al.  A Study of Sentiment Analysis: Concepts, Techniques, and Challenges , 2019, Proceedings of International Conference on Computational Intelligence and Data Engineering.

[20]  Eric Michael Clark,et al.  Applications In Sentiment Analysis And Machine Learning For Identifying Public Health Variables Across Social Media , 2019 .

[21]  B. Roy,et al.  Emotion regulation moderates the association between chronic stress and cardiovascular disease risk in humans: a cross-sectional study , 2018, Stress.

[22]  Shamim Nemati,et al.  How is the Doctor Feeling? ICU Provider Sentiment is Associated with Diagnostic Imaging Utilization , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[23]  Manik Sharma,et al.  An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders , 2018, EAI Endorsed Trans. Scalable Inf. Syst..

[24]  Ara Darzi,et al.  Sentiment Analysis of Health Care Tweets: Review of the Methods Used , 2018, JMIR public health and surveillance.

[25]  Mincheol Whang,et al.  Emotion Recognition Through Cardiovascular Response in Daily Life Using KNN Classifier , 2017, CSA/CUTE.

[26]  Susan Mengel,et al.  A Preliminary Investigation with Twitter to Augment CVD Exposome Research , 2017, BDCAT.

[27]  Victoria Bobicev,et al.  Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective , 2017, RANLP.

[28]  M. Besharat,et al.  The relationship between worry and anger rumination with adjustment problems to heart disease: The mediating role of difficulties in emotion regulation , 2017 .

[29]  E. Kim,et al.  Routine Angiographic Follow-Up versus Clinical Follow-Up after Percutaneous Coronary Intervention in Acute Myocardial Infarction , 2017, Yonsei medical journal.

[30]  A. Clark,et al.  The Impact and Implications of Twitter for Cardiovascular Medicine. , 2017, Journal of Cardiac Failure.

[31]  A. Troussov,et al.  The Demographics of Social Media Users in the Russian-Language Internet , 2017 .

[32]  M. F. Silveira,et al.  Conhecimento e atitude de pacientes com diabetes mellitus da Atenção Primária à Saúde , 2017 .

[33]  Andrzej Majkowski,et al.  Emotion recognition using facial expressions , 2017, ICCS.

[34]  Raymond Chiong,et al.  Multilingual sentiment analysis: from formal to informal and scarce resource languages , 2016, Artificial Intelligence Review.

[35]  D. Asch,et al.  Twitter as a Potential Data Source for Cardiovascular Disease Research. , 2016, JAMA cardiology.

[36]  Sophia Ananiadou,et al.  Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts , 2016, J. Biomed. Informatics.

[37]  Nik Bessis,et al.  Big data‐based extraction of fuzzy partition rules for heart arrhythmia detection: a semi‐automated approach , 2016, Concurr. Comput. Pract. Exp..

[38]  Davide Marengo,et al.  Sharing feelings online: studying emotional well-being via automated text analysis of Facebook posts , 2015, Front. Psychol..

[39]  Yihan Deng,et al.  Sentiment analysis in medical settings: New opportunities and challenges , 2015, Artif. Intell. Medicine.

[40]  Abeed Sarker,et al.  Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features , 2015, J. Am. Medical Informatics Assoc..

[41]  Gregory J. Park,et al.  Psychological Language on Twitter Predicts County-Level Heart Disease Mortality , 2015, Psychological science.

[42]  Walaa Medhat,et al.  Sentiment analysis algorithms and applications: A survey , 2014 .

[43]  Joan Y. Chiao,et al.  Eye movements during emotion recognition in faces. , 2014, Journal of vision.

[44]  Andreas Holzinger,et al.  Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online , 2013, Journal of medical Internet research.

[45]  A. Blenkinsopp,et al.  Medication reviews , 2012, British journal of clinical pharmacology.

[46]  Maegan S. Reynolds Risk of Acute Myocardial Infarction after the Death of a Significant Person in One's Life: The Determinants of Myocardial Infarction Onset Study , 2012 .

[47]  J. Muller,et al.  Risk of Acute Myocardial Infarction After the Death of a Significant Person in One's Life: The Determinants of Myocardial Infarction Onset Study , 2012, Circulation.

[48]  J. O’Keefe,et al.  Psychological Risk Factors and Cardiovascular Disease: Is it All in Your Head? , 2011, Postgraduate medicine.

[49]  Marie-Francine Moens,et al.  A machine learning approach to sentiment analysis in multilingual Web texts , 2009, Information Retrieval.

[50]  C. Tennant,et al.  The Impact of Emotions on Coronary Heart Disease Risk , 2001, Journal of cardiovascular risk.

[51]  S. Sakamoto,et al.  Hanshin-Awaji earthquake as a trigger for acute myocardial infarction. , 1997, American heart journal.

[52]  K. Shadan,et al.  Available online: , 2012 .