goemotions

Emotion classification in short Reddit comments using the GoEmotions dataset: baseline to Transformer model trained from scratch with explainability analysis (SHAP).

Reproducible GoEmotions annotation study with label frequencies, co-occurrence analysis, split comparisons, and a local browser viewer.

Emotion by Hoverfly: on-device emotion detection for Kotlin Multiplatform (Android, iOS, macOS, JVM, JS, Wasm). Finds the emotions in an English message (28 GoEmotions labels) plus a mood. Pure Kotlin, no dependencies.

Emotional Earth Mover's Distance for fine-grained hierarchical emotion analysis (ADMA 2025): code and baselines

Full-stack application that analyzes emotional sentiment in text via GoEmotions dataset, to help users better understand what emotions messages, texts and emails convey.

Research on uncertainty quantification for social media analysis: emotion tagging, sentiment classification, and crisis triage under explicit error budgets.

Multi-label emotion classifier (28 GoEmotions categories) using a BiLSTM in PyTorch, served via FastAPI. Per-class threshold tuning on validation set.

Multi-label emotion classification on GoEmotions: a 3.8 MB linear model at 96% of BERT, served from FastAPI and in-browser JS

Fine-grained emotion classification on GoEmotions: TF-IDF, BiLSTM and RoBERTa compared, with per-class threshold tuning. Hands-on NLP semester project, Paris-Saclay.

Five-class emotion classification on GoEmotions using TF-IDF baselines and BERT.

Comparison of TF-IDF + Linear SVM and DistilRoBERTa for multi-label emotion detection using the GoEmotions dataset.

Emotion Classification using LSTM, GRU, BiLSTM + Attention, and DistilBERT with a Gradio deployment.

An end-to-end Deep Learning NLP pipeline and interactive Streamlit dashboard for classifying text into Ekman emotions. Features comparative analysis between modern Transformers (RoBERTa, DistilBERT) and Sequence models (BiLSTM, GRU) built with PyTorch.

EmotionRadar: Multi-label Emotion Detection from Text using Supervised and Unsupervised Machine Learning

Machine Learning project for automatic emotion detection in text using the Google GoEmotions dataset, NLP preprocessing, TF-IDF vectorization, and multiple classification models.

Analysis of Reddit posts from r/Anxiety to explore symptoms, co-mentioned conditions, and emotional narratives using NLP methods. Modular Colab notebooks provided for reproducible research.

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.

🧬AI Sentiment Analysis project using NLP and Machine Learning with TF-IDF, multiple model comparisons, hyperparameter tuning, and advanced emotion detection using RoBERTa, Huggingface

Machine learning projects spanning regression, computer vision, and NLP. Includes RBF-based linear regression with regularization, MLP/CNN/ResNet models for OrganAMNIST classification, and traditional ML vs. BERT-based emotion classification on the GoEmotions dataset.

A full end-to-end NLP framework for analysing mental-health expressions on Reddit using sentiment modelling, fine-tuned classification (28 GoEmotions & 7 Ekman), circumplex affect modelling, and emotion network analysis.