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.
%0 Conference Paper
%1 AAAI148479
%A Cambria, Erik
%A Olsher, Daniel
%A Rajagopal, Dheeraj
%B AAAI Conference on Artificial Intelligence
%D 2014
%K SenticNet knowledge_base ontology sentimental_analysis sentiments
%T SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis
%U http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8479
%X 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.
@inproceedings{AAAI148479,
abstract = {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.},
added-at = {2016-01-08T04:04:02.000+0100},
author = {Cambria, Erik and Olsher, Daniel and Rajagopal, Dheeraj},
biburl = {https://www.bibsonomy.org/bibtex/2b5b96483f039c05804279afbf3b7bd2e/hangdong},
booktitle = {AAAI Conference on Artificial Intelligence},
interhash = {fb91b52c3617d138355e65c6536700ef},
intrahash = {b5b96483f039c05804279afbf3b7bd2e},
keywords = {SenticNet knowledge_base ontology sentimental_analysis sentiments},
timestamp = {2016-01-08T04:04:02.000+0100},
title = {SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis},
url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8479},
year = 2014
}