Can you tell? SSNet - a Biologically-inspired Neural Network Framework for Sentiment Classifiers

Published

March 4, 2021

Author(s)

Apostol Vassilev, Munawar Hasan, Honglan Jin

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 robust, accurate and computationally efficient classifiers for sentiment analysis and study several different realizations. Among them, we propose a systematic new approach to combining multiple predictions based on a dedicated neural network and develop mathematical analysis of it along with state-of-the-art experimental results. We also propose a heuristic-hybrid technique for combining models and back it up with experimental results on a representative benchmark dataset and comparisons to other methods to show the advantages of the new approaches.

Citation

arXiv

Pub Weblink

https://arxiv.org/abs/2006.12958v3

Pub Type

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Keywords

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

Citation

Vassilev, A. , Hasan, M. and Jin, H. (2021), Can you tell? SSNet - a Biologically-inspired Neural Network Framework for Sentiment Classifiers, arXiv, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932036, https://arxiv.org/abs/2006.12958v3 (Accessed April 25, 2024)