SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis

In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society. While often better than its human counterparts in classification and pattern recognition tasks, however, AI still struggles with complex tasks that require commonsense reasoning such as natural language understanding. In this context, the key limitations of current AI models are: dependency, reproducibility, trustworthiness, interpretability, and explainability. In this work, we propose a commonsense-based neurosymbolic framework that aims to overcome these issues in the context of sentiment analysis. In particular, we employ unsupervised and reproducible subsymbolic techniques such as auto-regressive language models and kernel methods to build trustworthy symbolic representations that convert natural language to a sort of protolanguage and, hence, extract polarity from text in a completely interpretable and explainable manner.

Automated summary

This paper proposes a commonsense-based neurosymbolic framework to address the limitations of dependency, reproducibility, trustworthiness, interpretability, and explainability in artificial intelligence for sentiment analysis.