Performance and Complexity Analysis of Neural Architectures for Emotion Classification Using Word Embeddings

Q: What does this research mean for the field?

Different deep learning architectures (RNN, LSTM, GRU, Seq2Seq, and Transformer Encoder) combined with pre-trained word embeddings exhibit varying performance and complexity trade-offs in fine-grained multi-class emotion classification. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Bindeshwar Singh Kushwaha Kushwaha

doi:10.5281/zenodo.22105447

August 26, 2026Open Access

Performance and Complexity Analysis of Neural Architectures for Emotion Classification Using Word Embeddings

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Key Points

Evaluate and compare the performance and structural complexity of five deep learning architectures for fine-grained multi-class emotion classification using pre-trained word embeddings.
Assessed five neural network models: RNN, LSTM, GRU, Seq2Seq (LSTM Encoder), and Transformer Encoder across six emotion categories (sadness, joy, love, anger, fear, and surprise).
Employed pre-trained Word2Vec and GloVe word embeddings to capture underlying semantic text representations.
Analyzed baseline embedding distributions and fine-tuned model outputs using t-SNE and UMAP dimensionality reduction visualizations.
Characterized performance and architectural complexity trade-offs across the five deep learning models for fine-grained emotion classification.
Demonstrated that task-specific fine-tuning reorganizes baseline Word2Vec and GloVe semantic embeddings into distinguishable emotion-specific clusters.

Implication

Comparative study evaluates deep neural architectures across six emotion classes, revealing structural shifts in fine-tuned semantic representations.

Abstract

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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Cite This Study

Bindeshwar Singh Kushwaha Kushwaha (2026) studied this question.

synapsesocial.com/papers/6a914631d15324a1df3a9a8d https://doi.org/10.5281/zenodo.22105447

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Performance and Complexity Analysis of Neural Architectures for Emotion Classification Using Word Embeddings

Key Points

Implication

Abstract

Discussion

Cite This Study

Performance and Complexity Analysis of Neural Architectures for Emotion Classification Using Word Embeddings

Key Points

Implication

Abstract

Discussion

Cite This Study

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