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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
List of contents
Introduction.- Literature Survey.- SenticNet.- Contribution to Sentiment Analysis.- Conclusion and Future Work.- Index.
About the author
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.
Summary
Offers a novel approach to combine computational complexity and machine learning
Demonstrates a construction of Medical Lexicon for the sole purpose of Sentiment Analysis in Bio-medical Domain
Includes a special chapter on experimental results obtained on the medical lexicon built
