TY  - GEN
N2  - 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.
AB  - 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.
T1  - The senticnet sentiment lexiconexploring semantic richness in multi-word concepts /
AU  - Biagioni, Raoul,
VL  - volume 4
CN  - P325.5.D38
ID  - 755641
KW  - Semantics
KW  - Computational linguistics.
SN  - 9783319389714
SN  - 3319389718
TI  - The senticnet sentiment lexiconexploring semantic richness in multi-word concepts /
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-38971-4
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-38971-4
ER  -