SenticNet

Citation

Satapathy R, Cambria E & Hussain A (2018) SenticNet. In: Satapathy R, Cambria E & Hussain A (eds.) Sentiment Analysis in the Bio-Medical Domain: Techniques, Tools, and Applications. Socio-Affective Computing. Amsterdam: Springer Publishing Company, pp. 39-103. https://doi.org/10.1007/978-3-319-68468-0_3

Abstract
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