Based on Data Balancing and Model Improvement for Multi-Label Sentiment Classification Performance Enhancement

• URL: http://arxiv.org/abs/2511.14073v2
• Date: Wed, 19 Nov 2025 14:56:35 GMT
• Title: Based on Data Balancing and Model Improvement for Multi-Label Sentiment Classification Performance Enhancement
• Authors: Zijin Su, Huanzhu Lyu, Yuren Niu, Yiming Liu,
• Abstract summary: Multi-label sentiment classification plays a vital role in natural language processing by detecting multiple emotions within a single text.<n>Existing datasets like GoEmotions often suffer from severe class imbalance, which hampers model performance.<n>We constructed a balanced multi-label sentiment dataset using GoEmotions data, emotion-labeled samples from Sentiment140, and manually annotated texts.<n> Experimental results demonstrate significant improvements in accuracy, precision, recall, F1-score, and AUC compared to models trained on imbalanced data.
• Score: 5.149011601951617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label sentiment classification plays a vital role in natural language processing by detecting multiple emotions within a single text. However, existing datasets like GoEmotions often suffer from severe class imbalance, which hampers model performance, especially for underrepresented emotions. To address this, we constructed a balanced multi-label sentiment dataset by integrating the original GoEmotions data, emotion-labeled samples from Sentiment140 using a RoBERTa-base-GoEmotions model, and manually annotated texts generated by GPT-4 mini. Our data balancing strategy ensured an even distribution across 28 emotion categories. Based on this dataset, we developed an enhanced multi-label classification model that combines pre-trained FastText embeddings, convolutional layers for local feature extraction, bidirectional LSTM for contextual learning, and an attention mechanism to highlight sentiment-relevant words. A sigmoid-activated output layer enables multi-label prediction, and mixed precision training improves computational efficiency. Experimental results demonstrate significant improvements in accuracy, precision, recall, F1-score, and AUC compared to models trained on imbalanced data, highlighting the effectiveness of our approach.

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