The abundance of text available in social media and health-related forums and blogs have recently attracted the interest of the public health community to use these sources for opinion mining. This book presents a lexicon-based approach to sentiment analysis in the bio-medical domain, i.e., WordNet for Medical Events (WME). This book gives an insight in handling unstructured textual data and converting it to structured machine-processable data in the bio-medical domain.
The readers will discover the following key novelties:
1) development of a bio-medical lexicon: WME expansion and WME enrichment with additional features.;
2) ensemble of machine learning and computational creativity;
3) development of microtext analysis techniques to overcome the inconsistency in social communication.
It will be of interest to researchers in the fields of socially-intelligent human-machine interaction and biomedical text mining
Introduction
Sentiment Analysis
Common Tasks in Web Minig
Computational Creativity
Biomedical text mining
The Problem of Sentiment Analysis
Literature Survey
Philosophy and Sentiments
Importance of Common Sense
Medical LexiconsDifferent Levels of Analysis
Microtext Analysis
Sentic Patterns
Semantic Parsing
Linguistic Rules
ELM Classifier
Evaluation
SenticNet
17 Knowledge Acquisition
18 Knowledge Representation
19 Knowledge-Based Reasoning
Contribution to Sentiment Analysis
20 Computation Creativity and Machine Learning
21 Extending Wordnet for Medical Events
22 Sentiment Extraction from Medical concepts/words
23 Addition of ConceptNet in WME
24 Semantic Network (SemNet) preparation
Conclusion and Future Work
25 Summary of Contributions
26 Deep Learning and its Applicaion in Medical Domain
27 Sentiment Analysis in Stock Market
Index
Mr. Ranjan Satapathy is currently pursuing Ph.D., at the School of Computer Science and Engg., NTU Singapore under the supervision of Dr. Erik Cambria. His major research interests are deep learning, sentiment analysis and natural language processing. He completed his Bachelor's degree in Computer Science and Engg., from IIIT-Bhubaneswar, India in 2013. He further recieved a M.Tech degree from University of Hyderabad, India in 2016, with majors in Computer Science. During his pursuits of Master's degree, he joined Dr. Cambria's research group SenticNet as an intern, where he worked on bio-medical sentiment analysis. This exposure and a keen-to-learn attitude motivated him to apply for Ph.D under Dr. Cambria.
