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

TL;DR

This paper introduces SSNet, a biologically inspired neural network architecture that combines multiple model predictions for sentiment analysis, demonstrating improved accuracy and efficiency through novel combination techniques.

Contribution

The paper presents a new neural network framework inspired by the sagittal stratum, with innovative methods for combining model predictions and comprehensive mathematical and experimental validation.

Findings

State-of-the-art experimental results on benchmark datasets

Effective combination of multiple predictions improves accuracy

Mathematical analysis supports the proposed methods

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.