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

Erik Cambria, Daniel Olsher, Dheeraj Rajagopal

AAAI 2014 pp. 1515-1521

doi:10.1609/AAAI.V28I1.8928 /aaai/2014/cambria2014aaai-senticnet/

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.

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Text

Cambria et al. "SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.8928

Markdown

[Cambria et al. "SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/cambria2014aaai-senticnet/) doi:10.1609/AAAI.V28I1.8928

BibTeX

@inproceedings{cambria2014aaai-senticnet,
  title     = {{SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis}},
  author    = {Cambria, Erik and Olsher, Daniel and Rajagopal, Dheeraj},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {1515-1521},
  doi       = {10.1609/AAAI.V28I1.8928},
  url       = {https://mlanthology.org/aaai/2014/cambria2014aaai-senticnet/}
}