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

a tool for comparing the predictions of any text classifiers

An example of retails products classification using scikit and nltk -

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.

Project is a Natural Language Processing (NLP) and Machine Learning initiative designed to analyze global public sentiment and intent during the COVID-19 pandemic. By processing millions of Bing search queries, the system visualizes the core concerns of citizens (e.g., vaccine rumors, food shortages, mental health).

textCNN for long-text classification 文本分类

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.

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.

Spam message classifier using NLP, TF-IDF, and Multinomial Naive Bayes with 97%+ accuracy and precision is 1.

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.

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

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