Based on Data Balancing and Model Improvement for Multi-Label Sentiment Classification Performance Enhancement
This addresses performance issues in multi-label sentiment classification for NLP applications, but it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of class imbalance in multi-label sentiment classification by constructing a balanced dataset and developing an enhanced model, resulting in significant improvements in accuracy, precision, recall, F1-score, and AUC compared to models trained on imbalanced data.
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.