A fast, robust Python library to check for offensive language in strings.

FBOW (Fast Bag of Words) is an extremmely optimized version of the DBow2/DBow3 libraries.

A modern IDE for writing classical Chinese poetry 格律诗编辑程序

It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization

This repository consists of a complete guide on natural language processing (NLP) in Python where we'll learn various techniques for implementing NLP including parsing & text processing and understand how to use NLP for text feature engineering.

This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.

Simple NLP in Rust with Python bindings

🍊 📄 Text Mining add-on for Orange3

NLP Functions for amplifying negations, managing elisions, creating ngrams, stems, phonetic codes to tokens and more.

This Python module can be used to obtain antonyms, synonyms, hypernyms, hyponyms, homophones and definitions.

Hybrid Code Networks https://arxiv.org/abs/1702.03274

This Repository Contains Solution to the Assignments of the Natural Language Processing Specialization from Deeplearning.ai on Coursera Taught by Younes Bensouda Mourri, Łukasz Kaiser, Eddy Shyu

Bag of visual words (BOVW) is commonly used in image classification. Its concept is adapted from information retrieval and NLP’s bag of words (BOW).

My works for EE 569 - Digital Image Processing - Spring 2018 - Graduate Coursework at USC - Dr. C.-C. Jay Kuo

Train a model with data from Firestore, save it to Cloud Storage and make predictions in Cloud Functions - entirely using NodeJS

D-Lab's 9 hour introduction to text analysis with Python. Learn how to perform bag-of-words, sentiment analysis, topic modeling, word embeddings, and more, using scikit-learn, NLTK, gensim, and spaCy in Python.

NLP tutorial

BERT 기반의 문맥을 반영한 한국어 토픽 모델링 (BERT Contextualized Topic Models)

Solve classical computer vision topic, image recognition, with simplest method, tiny images and KNN(K Nearest Neighbor) classification, and then move forward to the state-of-the-art techniques, bags of quantized local features and linear classifiers learned by SVC(support vector classifier).

Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.