Sentiment Analysis in the Bio-Medical Domain (eBook)
XXIV, 134 Seiten
Springer International Publishing (Verlag)
978-3-319-68468-0 (ISBN)
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 miningMr. 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.
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.
IntroductionSentiment Analysis Common Tasks in Web Minig Computational CreativityBiomedical text miningThe Problem of Sentiment AnalysisLiterature Survey Philosophy and SentimentsImportance of Common SenseMedical LexiconsDifferent Levels of Analysis Microtext Analysis Sentic Patterns Semantic Parsing Linguistic RulesELM Classifier Evaluation SenticNet 17 Knowledge Acquisition 18 Knowledge Representation 19 Knowledge-Based Reasoning Contribution to Sentiment Analysis20 Computation Creativity and Machine Learning 21 Extending Wordnet for Medical Events 22 Sentiment Extraction from Medical concepts/words23 Addition of ConceptNet in WME 24 Semantic Network (SemNet) preparationConclusion and Future Work25 Summary of Contributions 26 Deep Learning and its Applicaion in Medical Domain27 Sentiment Analysis in Stock Market Index
| Erscheint lt. Verlag | 23.1.2018 |
|---|---|
| Reihe/Serie | Socio-Affective Computing |
| Zusatzinfo | XXIV, 134 p. 45 illus., 33 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Medizin / Pharmazie ► Studium | |
| Schlagworte | Biomedical text mining • Computational Complexity • machine learning • Natural Language Processing • sentiment analysis |
| ISBN-10 | 3-319-68468-X / 331968468X |
| ISBN-13 | 978-3-319-68468-0 / 9783319684680 |
| Haben Sie eine Frage zum Produkt? |
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