SenticNet 9: Generative Commonsense for Emotion AI via Conceptual Primitive Discovery and Time Shift Mechanism
Abstract
Large language models (LLMs) generate fluent, context-rich text but suffer from hallucinations and limited interpretability. We introduce SenticNet 9, a neurosymbolic framework that automates commonsense reasoning while preserving transparency. It leverages conceptual primitive discovery (CPD) to learn foundational concepts and a time shift mechanism (TSM) to iteratively refine them through temporal feedback. This combination yields a scalable, cognitively inspired architecture that merges symbolic interpretability with LLM generalization. Experiments show SenticNet 9 outperforming embeddings, transformers, and state-of-the-art LLMs across tasks, delivering higher accuracy without sacrificing explainability.
Fields of Study & Subjects
Emotion and Mood Recognition
Sentiment Analysis and Opinion Mining
Mental Health via Writing
Publication details
- Journal / source
- IEEE Transactions on Computational Social Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 13
- Issue
- 3
- Pages
- 4086–4095
- Publication date
- 28 April 2026
- Language
- EN