Contents -- 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 Clinicalnarratives -- .ix x Contents -- 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. -- Contents xi -- Lexicon-based medical sentiment analysis. -- 1 Overview on lexicon-based approaches. -- 2 Approaches to lexicon generation -- achine learning-based sentiment analysis approaches -- .1 Unsupervised learning approaches . -- .1.1 Partition methods -- 1.2 Hierarchical clustering methods. -- 1.2 Supervised approaches -- .2.1 Linear approaches -- .2.2 Probabilistic approaches. -- 2.3 Rule-based classifier -- .2.4 Decision tree classifier. -- .3 Semi-supervised approaches. . -- .4 Deep learning approaches -- .4.1 Deep neural networks (DNN) -- .4.2 Convolutional neural networks (CNN) -- .4.3 Long short-term memory (LSTM). -- 11.5 Hybrid approaches -- 11.6 Concluding remarks -- 12 Sentiment analysis tools -- 12.1 Sentiment: 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 LIWC83 -- 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 -- xii Contents -- 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 Threats101 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 -- Contents xiii 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 Demonstrate the benefit for patient care. -- 18.6 Concluding remarks -- References -- Glossary. -- Index.