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EmojiComplete
An NLP model that recommends sentiment-relevant emojis from input sentences using DistilBERT embeddings and a two-layer bidirectional LSTM.
- Signal
- Language modeling · dataset design · evaluation
- Focus
- Python · PyTorch · SLURM
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Developed EmojiComplete, an NLP model leveraging DistilBERT to predict emojis from full-sentence meaning and sentiment, improving contextual relevance beyond word-level autocorrect
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Built and curated a modern 150k-tweet dataset, introducing emoji clustering to reduce label noise and boost prediction accuracy across diverse demographics
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Achieved 39% top-1 and 78% top-5 accuracy, more than doubling prior benchmarks (DeepMoji), validating performance for real-world messaging and autocomplete applications