All NLP you Need Here. 目前包含15个NLP demo的pytorch实现（大量代码借鉴于其他开源项目，原先是自己玩的，后来干脆也开源出来）
Fine-tuned pre-trained GPT2 for custom topic specific text generation. Such system can be used for Text Augmentation.
Fake News Detection - Feature Extraction using Vectorization such as Count Vectorizer, TFIDF Vectorizer, Hash Vectorizer,. Then used an Ensemble model to classify whether the news is fake or not.
TextSentimentClassification, using tensorflow.
Naive Bayes algorithm for multi-category text classification.
Several methods to solve text classification problems
This is a toy implementation of RNNText on zhihu tag classification dataset.
FastTextClassification is all you need for text classification! Fast text classification for everyone, enjoy your NLP journey! 这可能是目前为止最全面的开源文本分类项目，支持中英双语、多种模型、多种任务。
Apply CNN-LSTM model on multi-class text classification task.
Source code for Is this fake news! 🔥
Text Classification Competition for Sarcasm Detection in Tweets using BERT model
A unique sentiment analysis model on IMDB reviews with custom negation handling. Instead of generic preprocessing, it smartly tags words after negators like "not" (e.g., "not good" → "not_good"), preserving sentiment context. Comparison of models with and without this logic shows improved accuracy and real-world reliability.
Language Detector Loads and cleans text data, trains a language classification model using TF-IDF and Logistic Regression, evaluates it, and enables interactive language prediction with saved model reuse.
Sentimental Analysis/Natural Language Processing in Python
Turns a bank statement CSV into a financial plan: ML transaction categorisation, spending-behaviour insights, subscription and anomaly detection, month-end forecasting, an explainable 0-100 health score, and a ranked savings + debt strategy. FastAPI + Streamlit, optional Claude layer. 95 tests.
AI-powered support ticket categorization using NLP, TF-IDF, and Multinomial Naive Bayes with confidence-based routing.
AILAW는 대표적인 전문가용 문서의 하나인 판례에서의 범죄 사실에 대한 주요 정보를 자동으로 추출하기 위한 데이터셋 및 방법론을 제안합니다.
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