We use LSTM, BiLSTM, BERT and SVM with TF-IDF, Word2vec and Bag-of-words to classify this documents to positive (labeled as 1), neutral (labeled as 0) and negative (labeled as 2)

对20 newsgroups 数据集 进行文本分类

An example of retails products classification using scikit and nltk -

a tool for comparing the predictions of any text classifiers

This is about spam classification using HMM model in python language

Text processing and summarize with the category web application for Arabic and English texts using NLTK, Python, Flask, and other web languages.

Hierarchical Multi Label Hate Speech and Abusive Language Classification

Parse movie scripts for linguistic analysis

ML classifier application with Tensorflow and Django/Celery

Hermes is an open-source AI-powered system for automated message filtering in Telegram groups, notifying users only about relevant content through a modular container-based architecture.

Analysis and Visualizations for COVID-19 Bing search engine queries + Classifier pipeline for predicting country based on search query.

scraping bbc news with scrapy, cleanse and store them to public MongoDB database and provide public APIs with AWS, including text-classification example with machine-learning algorithm to predict tag text from BBC news article text.

TextPredict is a powerful Python package designed for various text analysis and prediction tasks using advanced NLP models. It simplifies the process of performing sentiment analysis, emotion detection, zero-shot classification, named entity recognition (NER), and more.

textCNN for long-text classification 文本分类

Note : This Repository consists files of the NLP Project - Fake News Detection Classifier which was held as a Data Science assessment by Techigai ,Hyd.

Detects fake product reviews using supervised ML algorithms like SVM, Random Forest, and XGBoost. Uses NLP techniques (tokenization, lemmatization, TF-IDF) for preprocessing. SVM achieved the highest accuracy and F1-score. Aims to enhance trust in online review systems.

💬 It uses NLP techniques to classify reviews as positive, neutral or negative, providing valuable insights into customer feedback.

🛡️SMSGuard – An advanced Machine Learning–powered SMS Spam Detection system using TF-IDF and models like Naive Bayes, Logistic Regression, and SVM. Includes confusion matrix visualization, real-message testing, and custom SMS predictions. Perfect for cybersecurity, telecom filtering, and ML learning.

A lightweight sentiment analysis demo powered by Large Language Models (LLMs) using the Prompture package.