Sentiment Analysis in the Bio-Medical Domain: Techniques, Tools, and Applications

ISBN-13: 9783319684673 / Angielski / Twarda / 2018 / 134 str.

Ranjan Satapathy; Erik Cambria; Amir Hussain

Spis treści

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

O autorze

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.

Blurb

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