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Sentiment Analysis in the Bio-Medical Domain

- Techniques, Tools, and Applications

Bag om Sentiment Analysis in the Bio-Medical Domain

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

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783319684673
  • Indbinding:
  • Hardback
  • Sideantal:
  • 134
  • Udgivet:
  • 1. februar 2018
  • Udgave:
  • 12017
  • Vægt:
  • 458 g.
  • BLACK NOVEMBER
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Leveringstid: 2-3 uger
Forventet levering: 26. november 2024

Beskrivelse af Sentiment Analysis in the Bio-Medical Domain

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

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