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

Authors

  • Erik Cambria Nanyang Technological University
  • Daniel Olsher Carnegie Mellon University
  • Dheeraj Rajagopal National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v28i1.8928

Keywords:

SenticNet, concept-level sentiment analysis, biologically-inspired opinion mining, sentic computing, knowledge representation

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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Published

2014-06-21

How to Cite

Cambria, E., Olsher, D., & Rajagopal, D. (2014). SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8928

Issue

Section

Main Track: NLP and Knowledge Representation