Sentic neural networks: A novel cognitive model for affective common sense reasoning

Mazzocco, T.; Cambria, E.; Hussain, A.; Wang, Q.-F.

Abstract

In human cognition, the capacity to reason and make decisions is strictly dependent on our common sense knowledge about the world and our inner emotional states: we call this ability affective common sense reasoning. In previous works, graph mining and multi-dimensionality reduction techniques have been employed in attempt to emulate such a process and, hence, to semantically and affectively analyze natural language text. In this work, we exploit a novel cognitive model based on the combined use of principal component analysis and artificial neural networks to perform reasoning on a knowledge base obtained by merging a graph representation of common sense with a linguistic resource for the lexical representation of affect. Results show a noticeable improvement in emotion recognition from natural language text and pave the way for more bio-inspired approaches to the emulation of affective common sense reasoning.

Citation

Mazzocco, T., Cambria, E., Hussain, A., & Wang, Q.-F. (2012, July). Sentic neural networks: A novel cognitive model for affective common sense reasoning. Presented at BICS: International Conference on Brain Inspired Cognitive Systems, Shenyang, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: BICS: International Conference on Brain Inspired Cognitive Systems
• Start Date: Jul 11, 2012
• End Date: Jul 14, 2012
• Publication Date: 2012
• Deposit Date: Oct 17, 2019
• Publisher: Springer
• Pages: 12-21
• Series Title: Lecture Notes in Computer Science
• Series Number: 7366
• Series ISSN: 0302-9743
• Book Title: Advances in Brain Inspired Cognitive Systems
• ISBN: 978-3-642-31560-2
• DOI: https://doi.org/10.1007/978-3-642-31561-9_2
• Keywords: AI, NLP, Neural Networks, Cognitive Modeling, Sentic Computing
• Public URL: http://researchrepository.napier.ac.uk/Output/1793270