Reading Between the Lines: Emotion Classification using Sentence Transformers

Traditional feature extraction methods have struggled to capture nuanced emotional expressions while, transformer-based approaches although more effective often depends on computationally expensive fine-tuning. To address these issues, this research explores the use of Sentence Transformer embeddings as fixed feature extractors for emotion classification tasks. Experiments were conducted on ISEAR benchmark dataset, where ML classifiers were trained on embedding generated from four base pre-trained sentence transformers. The evaluation was performed using both holdout and Stratified 5-Fold cross-validation strategies. Among the tested approaches, using stratified 5-fold cross validation, the E5-base v2 (BERT Based) embedding model combined with a support vector classifier achieved an accuracy of 69.90%, outperforming proprietary embedding baselines without the need for fine tuning. Statistical validation performed using paired t-tests further confirmed that performance differences between embedding models are significant and not attributed to random data partitioning. Additionally, empirical finding indicates that embedding fusion of BERT based pre trained sentence transformers (BGE, E5, and GTE) performance is marginally comparable to that of classifier trained on individual embedding using same evaluation strategy. These results demonstrate that open-source sentence embeddings constitute a computationally efficient and competitive alternative to fine-tuned transformer models for emotion classification tasks.