Pytorch Implementation of GoEmotions 😍😢😱
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Updated
Jun 12, 2023 - Python
Pytorch Implementation of GoEmotions 😍😢😱
Korean version of GoEmotions Dataset 😍😢😱
Text Emotion classification using BERT-LSTM model on GoEmotions dataset
Emotional Earth Mover's Distance for fine-grained hierarchical emotion analysis (ADMA 2025): code and baselines
A research project comparing Classical ML models, Hybrid LLM-embedding methods, and fine-tuned Transformers for emotion classification. The study evaluates performance on two datasets, song lyrics and GoEmotions, to analyze how model choice and dataset quality impact accuracy and generalization.
Full-stack application that analyzes emotional sentiment in text via GoEmotions dataset, to help users better understand what emotions messages, texts and emails convey.
A group project for the Essentials of Text and Speech Processing module at UZH.
Five-class emotion classification on GoEmotions using TF-IDF baselines and BERT.
Reproduction code and five-seed results for a factor-isolation study of the CLFER-LT contrastive stack (CB-Focal + Jaccard LT-MulSupCon + memory queue + LPC) on the official GoEmotions multi-label long-tailed split.
Research on uncertainty quantification for social media analysis: emotion tagging, sentiment classification, and crisis triage under explicit error budgets.
A study project to practice TensorFlow - building a sentiment analysis model. Employed GloVe embeddings in conjunction with the GoEmotions dataset.
Comparison of TF-IDF + Linear SVM and DistilRoBERTa for multi-label emotion detection using the GoEmotions dataset.
Multi-label emotion classification on GoEmotions: a 3.8 MB linear model at 96% of BERT, served from FastAPI and in-browser JS
RoBERTa-large GoEmotions emotion classifier with focal loss, tuned thresholds, and test macro-F1 0.5330.
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