Can you tell? SSNet - a Sagittal Stratum-inspired Neural Network Framework for Sentiment Analysis

Published

September 24, 2020

Author(s)

Apostol T. Vassilev, Munawar Hasan

Abstract

When people try to understand nuanced language they typically process multiple input sensor modalities to complete this cognitive task. It turns out the human brain has even a specialized neuron formation, called sagittal stratum, to help us understand sarcasm. We use this biological formation as the inspiration for designing a neural network architecture that combines predictions of different models on the same text to construct a robust, accurate and computationally efficient classifier for sentiment analysis. Experimental results on representative benchmark datasets and comparisons to other methods show the advantages of the new network architecture. [This is an updated version to the paper published in June 2020].

Citation

arXiv

Volume

2006

Pub Weblink

https://arxiv.org/abs/2006.12958

Pub Type

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Keywords

natural language processing, machine learning, deep learning, artificial intelligence

Citation

Vassilev, A. and Hasan, M. (2020), Can you tell? SSNet - a Sagittal Stratum-inspired Neural Network Framework for Sentiment Analysis, arXiv, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931152, https://arxiv.org/abs/2006.12958 (Accessed May 18, 2026)

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