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Most Influential AAAI 2014 Paper · 2026-03 edition

SenticNet 3: A Common And Common-Sense Knowledge Base For Cognition-Driven Sentiment Analysis

Erik Cambria; Daniel Olsher; Dheeraj Rajagopal

Venue
AAAI Conference on Artificial Intelligence (AAAI) 2014
Recognition
Most Influential AAAI 2014 Paper (Rank No. 6)
Edition
2026-03
Impact factor
6
Certificate ID
12ee388fefccb107

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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