Sentiment Analysis in the Medical Domain

Preface
Contents
Acronyms
Part I Landscape of Medical Sentiment
1 What is Special about Medical Sentiment Analysis?
	1.1 Overview
	1.2 Opinion Definition
	1.3 Definition of Medical Sentiment
2 Use Cases of Medical Sentiment Analysis
	2.1 Sentiment Analysis in Mental Health
	2.2 Outcome and Quality Assessment of Healthcare Services and Technologies
		2.2.1 Analysis of Patient Questionnaires
		2.2.2 Clinical Outcome Analysis
		2.2.3 Social Media as Mirror of Service Quality
	2.3 Sentiment Analysis for Clinical Risk Prediction
	2.4 Sentiment Analysis for Public Health
	2.5 Sentiment Analysis for Pharmacovigilance
	2.6 Sentiment and Emotion Analysis in Health-Related Conversational Agents
Part II Resources and Challenges
3 Medical Social Media and Its Characteristics
	3.1 Characteristics of Medical Social Media Data
	3.2 Twitter
	3.3 User Reviews
	3.4 Forums
4 Clinical Narratives and Their Characteristics
	4.1 Linguistic Characteristics of Clinical Narratives
	4.2 Clinical Narratives
5 Other Data Sources
	5.1 User Statements from Interaction with Intelligent Agents
	5.2 Other Sources
6 Datasets for Medical Sentiment Analysis
	6.1 The Burden of Available Datasets
	6.2 MIMIC Databases
	6.3 i2B2 Dataset
	6.4 TREC Dataset
	6.5 eDiseases Dataset
	6.6 Multimodal Sentiment Analysis Challenge (MuSe)
	6.7 General Domain Datasets
7 Lexical Resources for Medical Sentiment Analysis
	7.1 LIWC
	7.2 SentiWordNet and Its Derivations
	7.3 AFINN
	7.4 EmoLex
	7.5 WordNet Affect
	7.6 WordNet for Medical Events
	7.7 Other Sentiment Lexicons
	7.8 Ontologies and Biomedical Vocabularies
Part III Solutions
8 Levels and Tasks of Sentiment Analysis
	8.1 Level of Analysis
		8.1.1 Document-Level Sentiment Analysis
		8.1.2 Sentence-Level Sentiment Analysis
		8.1.3 Aspect-Level Sentiment Analysis
	8.2 Tasks Within Medical Sentiment Analysis
		8.2.1 Subjectivity Analysis
		8.2.2 Polarity Analysis
		8.2.3 Intensity Classification
		8.2.4 Emotion Recognition
9 Document Pre-processing
	9.1 Overview
	9.2 Data Collection and Preparation
	9.3 Text Normalisation
	9.4 Feature Extraction
		9.4.1 Bag of Words
		9.4.2 Distributed Representation
	9.5 Feature Selection
	9.6 Topic Detection
10 Lexicon-Based Medical Sentiment Analysis
	10.1 Overview on Lexicon-Based Approaches
	10.2 Approaches to Lexicon Generation
11 Machine Learning-Based Sentiment Analysis Approaches
	11.1 Unsupervised Learning Approaches
		11.1.1 Partition Methods
		11.1.2 Hierarchical Clustering Methods
	11.2 Supervised Approaches
		11.2.1 Linear Approaches
		11.2.2 Probabilistic Approaches
		11.2.3 Rule-Based Classifier
		11.2.4 Decision Tree Classifier
	11.3 Semi-supervised Approaches
	11.4 Deep Learning Approaches
		11.4.1 Deep Neural Networks (DNN)
		11.4.2 Convolutional Neural Networks (CNN)
		11.4.3 Long Short-Term Memory (LSTM)
	11.5 Hybrid Approaches
	11.6 Concluding Remarks
12 Sentiment Analysis Tools
	12.1 Sentiment 140 Sentiment Analysis Tool
	12.2 TextBlob
	12.3 Pattern for Python
	12.4 Valence Aware Dictionary and Sentiment Reasoner (VADER)
	12.5 TensiStrength
	12.6 LIWC
	12.7 Other Tools
13 Case Studies
	13.1 Learning About Suicidal Ideation
		13.1.1 The Problem
		13.1.2 Solution Overview
		13.1.3 Methods and Procedures
	13.2 Predicting the Psychiatric Readmission Risk
		13.2.1 The Problem
		13.2.2 Solution Overview
		13.2.3 Methods and Procedures
	13.3 Generating a Corpus for Clinical Sentiment Analysis
		13.3.1 The Problem
		13.3.2 Solution Overview
		13.3.3 Methods and Procedures
	13.4 Conversational Agent with Emotion Recognition
		13.4.1 The Problem
		13.4.2 Solution Overview
		13.4.3 Methods and Procedures
	13.5 Surveillance of Public Opinions in Times of Pandemics
		13.5.1 The Problem
		13.5.2 Solution Overview
		13.5.3 Methods and Procedures
	13.6 Providing Quality Information About Hospitals
		13.6.1 The Problem
		13.6.2 Solution Overview
		13.6.3 Methods and Procedures
Part IV Future
14 Medical Sentiment Analysis: Quo Vadis?
	14.1 SWOT Strategy
	14.2 Strengths
	14.3 Weaknesses
	14.4 Opportunities
	14.5 Threats
15 Open Challenges Related to Language
	15.1 Specific Language Phenomena Hampering Sentiment Analysis
		15.1.1 Negations
		15.1.2 Valence Shifters
		15.1.3 Paraphrasing, Sarcasm and Irony
		15.1.4 Comparative Sentences
		15.1.5 Coordination Structures
		15.1.6 Word Ambiguity
	15.2 Evolution of Language
16 Responsible Sentiment Analysis in Healthcare
	16.1 Ethical Principles Applied to Medical Sentiment Analysis
	16.2 Respect for Autonomy
	16.3 Beneficience and Non-maleficience
	16.4 Justice
	16.5 Explicability and Trust
	16.6 Concluding Remarks
17 Explainable Sentiment Analysis
	17.1 Definition and Need for XAI
	17.2 Explainable AI Methods
	17.3 Applications of XAI to Medical Sentiment Analysis
18 The Future of Medical Sentiment Analysis
	18.1 Current Research Gaps in Medical Sentiment Analysis
	18.2 Towards Domain-Specific Resources: Lexicons and Datasets
	18.3 Addressing Domain-Specific Challenges and Increasing Accuracy
	18.4 Towards Understandable and Ethical Sentiment Analysis
	18.5 Demonstrating the Benefits for Patient Care
	18.6 Concluding Remarks
Glossary
	Glossary
References
Index