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Springer Sentiment Analysis Bio-Medical Domain - Springer Computing

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Description

This Springer publication explores sentiment analysis techniques applied to bio-medical text sources, including social media and health forums. The book introduces a lexicon-based approach centred on WordNet for Medical Events (WME), designed to transform unstructured textual data into structured, machine-processable information within the bio-medical field. Readers will gain insight into developing specialised bio-medical lexicons and expanding medical event terminology for natural language processing applications. The work addresses the growing need for opinion mining in public health research, where understanding sentiment across online health discussions and patient forums has become increasingly valuable. Part of the Socio-Affective Computing series, this volume bridges artificial intelligence and medical informatics, offering practical techniques for researchers and practitioners working with bio-medical text data and machine learning applications in healthcare contexts.

Key Features

This Springer book presents a lexicon-based sentiment analysis approach specifically designed for bio-medical text sources and medical domain applications.

The WME (WordNet for Medical Events) framework enables conversion of unstructured health-related textual data into structured, machine-processable formats.

Techniques cover opinion mining from social media and health-related forums, supporting public health research and patient sentiment analysis.

The volume is part of the Socio-Affective Computing series, providing integrated coverage of AI methods and sentiment analysis in bio-medical computing.

Content addresses bio-medical lexicon development and WME expansion strategies for improved natural language processing in medical informatics.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
01 February 2018
Listed Since
24 August 2017

Barcode

No barcode data available

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