Sentiment analysis deals with extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. Medical sentiment analysis refers to the identification and analysis of sentiments or emotions expressed in free-textual documents with a scope on healthcare and medicine. This fascinating problem offers numerous application areas in the domain of medicine, but also research challenges. The book provides a comprehensive introduction to the topic. The primary purpose is to provide the necessary background on medical sentiment analysis, ranging from a description of the notions of medical sentiment to use cases that have been considered already and application areas of relevance. Medical sentiment analysis uses natural language processing (NLP), text analysis and machine learning to realise the process of extracting and classifying statements regarding expressed opinion and sentiment. The book offers a comprehensive overview on existing methods of sentiment analysis applied to healthcare resources or health-related documents. It concludes with open research avenues providing researchers indications which topics still have to be developed in more depth.
Author(s): Kerstin Denecke
Publisher: Springer
Year: 2023
Language: English
Pages: 150
City: Cham
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
