Comparative analysis reveals distinct performance tradeoffs among five neural architectures in fine-grained emotion classification, highlighting the efficacy of task-specific embedding fine-tuning.
The paper evaluates five deep learning architectures RNN, LSTM, GRU, Seq2Seq (LSTM Encoder), and Transformer Encoder for multi-class emotion classification across six fine-grained emotional categories (sadness, joy, love, anger, fear, and surprise). To capture underlying semantic representations, the study leverages pre-trained Word2Vec and GloVe word embeddings, analyzing both baseline embedding structures (via t-SNE and UMAP visualizations) and model performance following task-specific fine-tuning.
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Bindeshwar Singh Kushwaha Kushwaha (2026) studied this question.
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