SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis

被引：0

作者：

Cambria, Erik ^{[1
]}

Olsher, Daniel ^{[2
]}

Rajagopal, Dheeraj ^{[3
]}

机构：

[1] Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore

[2] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA

[3] Natl Univ Singapore, Temasek Labs, Singapore, Singapore

来源：

PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2014年

关键词：

OPINIONS; MODEL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

SenticNet is a publicly available semantic and affective resource for concept-level sentiment analysis. Rather than using graph-mining and dimensionality-reduction techniques, SenticNet 3 makes use of 'energy flows' to connect various parts of extended common and common-sense knowledge representations to one another. SenticNet 3 models nuanced semantics and sentics (that is, the conceptual and affective information associated with multi-word natural language expressions), representing information with a symbolic opacity of an intermediate nature between that of neural networks and typical symbolic systems.

引用

页码：1515 / 1521

页数：7

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