Advancing emotion recognition in social media: A novel integration of heterogeneous neural networks with fine-tuned language models

Abstract

Social media platforms have emerged as crucial sources for emotion analysis, but the issue of non-compliance in labeling by fine-tuned large language models (LLMs) can significantly impact the accuracy of emotion classification. This study addresses this challenge by introducing a novel compliance-driven training set that systematically harmonizes label discrepancies across multiple LLMs, thereby enhancing classification accuracy by over 5% on the non-compliance set. Integrating this compliance set with a Heterogeneous Neural Network (HNN) architecture, we propose a robust framework for emotion classification. Our approach is validated on three diverse datasets, GoEmotion, Friends, and TEC, demonstrating substantial improvements in accuracy, F1 score, and recall over baseline models. These results confirm the effectiveness of our compliance-driven strategy and establish a new benchmark for emotion recognition in social media content. The proposed framework offers a versatile and scalable solution applicable across various languages and platforms, ensuring broad utility in advanced emotion classification tasks.