The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts [recurso electrónico] / by Raoul Biagioni.
Tipo de material:
TextoSeries SpringerBriefs in Cognitive Computation ; 4Editor: Cham : Springer International Publishing : Imprint: Springer, 2016Descripción: VI, 55 p. 13 illus., 8 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319389714Tema(s): Medicine | Neurosciences | Computational linguistics | Semantics | Biomedicine | Neurosciences | Language Translation and Linguistics | SemanticsFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 612.8 Clasificación LoC:RC321-580Recursos en línea: Libro electrónico
Contenidos:
En: Springer eBooksResumen: The research and its outcomes presented in this book, is about lexicon-based sentiment analysis. It uses single-, and multi-word concepts from the SenticNet sentiment lexicon as the source of sentiment information for the purpose of sentiment classification. In 6 chapters the book sheds light on the comparison of sentiment classification accuracy between single-word and multi-word concepts, for which a bespoke sentiment analysis system developed by the author was used. This book will be of interest to students, educators and researchers in the field of Sentic Computing.
Introduction -- Sentiment Analysis -- SenticNet -- Unsupervised Sentiment Classification -- Evaluation -- Conclusion -- Index.
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
| Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Introduction -- Sentiment Analysis -- SenticNet -- Unsupervised Sentiment Classification -- Evaluation -- Conclusion -- Index.
The research and its outcomes presented in this book, is about lexicon-based sentiment analysis. It uses single-, and multi-word concepts from the SenticNet sentiment lexicon as the source of sentiment information for the purpose of sentiment classification. In 6 chapters the book sheds light on the comparison of sentiment classification accuracy between single-word and multi-word concepts, for which a bespoke sentiment analysis system developed by the author was used. This book will be of interest to students, educators and researchers in the field of Sentic Computing.
