{"repo":"AmirhosseinHonardoust/Sentiment-Analysis-BERT","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Sentiment-Analysis-BERT","clone":"git clone https://github.com/AmirhosseinHonardoust/Sentiment-Analysis-BERT.git","description":"End-to-end sentiment analysis of tweets using BERT. Includes preprocessing, training, and evaluation with classification reports, confusion matrices, ROC curves, and word clouds. Demonstrates fine-tuning of transformer models for text classification with modular, reproducible code.","language":"Python","stars":38,"topics":["bert","deep-learning","huggingface","machine-learning","nlp","pytorch","sentiment-analysis","text-classification","transformers","twitter"],"license":"MIT","category":"machine-learning","readme_excerpt":"Sentiment Analysis with BERT A deep learning project for sentiment classification of tweets using BERT (Bidirectional Encoder Representations from Transformers) . The project includes data preprocessing, vocabulary/tokenizer setup, model training, evaluation, and visualization of results such as confusion matrix, ROC curves, and word clouds. --- Features - Preprocess tweets with cleaning, tokenization, and splitting into train/val/test sets. - Fine-tune bert-base-uncased on sentiment labels ( negative , neutral , positive ). - Track and visualize training & validation loss . - Generate classification reports, confusion matrices, ROC curves . - Create word clouds for positive and negative predictions. - Modular codebase with reproducible pipelines for preprocessing, training, and evaluation. --- Project Structure --- Setup Preprocess Data Train BERT Evaluate --- Results - Classification Report: outputs/classification report.txt --- - Confusion Matrix: --- - Training Curves: --- - Negative Wordcloud: --- Requirements - Python 3.8+ - PyTorch - Transformers (HuggingFace) - Scikit-learn, Pandas, Matplotlib, Seaborn - WordCloud --- Next Steps - Expand dataset for more robust evaluation. - Try advanced transformer models (RoBERTa, DistilBERT). - Apply hyperparameter tuning and cross-validation. - Deploy model with FastAPI or Streamlit for interactive demo.","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Sentiment-Analysis-BERT/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}