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

• URL: http://arxiv.org/abs/2006.12958v4
• Date: Fri, 30 Jul 2021 20:10:43 GMT
• Title: Can you tell? SSNet -- a Sagittal Stratum-inspired Neural Network Framework for Sentiment Analysis
• Authors: Apostol Vassilev, Munawar Hasan, Honglan Jin
• Abstract summary: We propose 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. 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.
• Score: 1.0312968200748118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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