🦆 Contextually-keyed word vectors

Amazon SageMaker Local Mode Examples

Using pre trained word embeddings (Fasttext, Word2Vec)

Incremental learning of word embeddings with context informativeness.

A resume filtering based on natural language processing

Wikidata embedding

Text classification with Reuters-21578 datasets using Gensim Word2Vec and Keras LSTM

NLP with NLTK for Sentiment analysis amazon Products Reviews

Aspect-Based Sentiment Analysis

Ready to use Spanish Word2Vec embeddings created from >18B chars and >3B words

Deep Learning notes and practical implementation with Tensorflow and keras. Text Analytics and practical application implementation with NLTK, Spacy and Gensim.

Creating word embeddings from scratch and visualize them on TensorBoard. Using trained embeddings in Keras.

A simple web application for searching Word2Vec embeddings derived from approximately 2,000 law reports published by the The Incorporated Council of Law Reporting for England & Wales (https://www.iclr.co.uk).

Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Network

Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network

Sentiment analysis for Twitter's tweet (in Indonesia language) was built with 3 models to get a comparison in determining which model gives the best results for predicting a tweet to have a positive or negative meaning.

ArWordVec is a collection of pre-trained word embedding model built from huge repository of Arabic tweets in different topics. The aim of these models is to support the community in their Arabic NLP-based research.

Ensemble PhoBERT with FastText Embedding to improve performance on Vietnamese Sentiment Analysis tasks.

Code to run LDA algorithm on Twitter/Foursquare scraped data.

📷 Crawl and Analyze Instagram Hashtag Data: KoNLPY to gensim word2Vec & scikit-learn TF-IDF