Embedding模型代码和学习笔记总结

🎤 Building a content-based podcast recommender system using NLP

Romanian Word Embeddings. Here you can find pre-trained corpora of word embeddings. Current methods: CBOW, Skip-Gram, Fast-Text (from Gensim library). The .vec and .model files are available for download (all in one archive).

🐬 Reviewer recommendation system for Pull Requests in github using social network analysis and topic modeling.

userfriendly bot normally interacts as friend

Topic extractor with the idea of generating labels using genism.n_similarity

this repo contains files for my analysis on disney land visitor reviews using NLP

This project explores the realm of Natural Language Processing (NLP) using Word2Vec and FastText models. Dive into domain-specific embeddings, analyze clinical trials data related to Covid-19, and uncover the power of AI and ML in understanding textual data.🌟

Natural Language Processing for Google Play Applications. Where sentiment analysis was used on reviews to decide on new features to recommend developers.

Twitter Sentiment Analysis

FAQ Page which helps user in finding relevant answers for the questions using NLP

Exploratory Data Analysis on various data set.

Improve access to healthcare services and reduce costs.

Applying Word 2 Vec Algorithm on Reddit comments

The goal of the presidential vocabulary program is to find the most similar words to a given word based on all the speeches made by U.S. presidents. It uses Selenium to scrape transcripts of speeches from the Miller Center website.

This repository contains the implementation of our paper "Auto-labelling of Bug Report using Natural Language Processing". Our paper introduces an NLP-based method using bug report attributes, leveraging a neural network for retrieval.

Implementing Text Summarization techniques on 'CNN DialyMail' dataset, using both 'Extractive' and 'Abstractive' strategies.

The repository contains Jupyter notebooks for various NLP tasks.

News Article Insights is a Python project that uses the NewsAPI to fetch articles on specific companies and keywords. It extracts content and performs text analysis, including sentiment analysis, named entity recognition, and topic modeling, to uncover trends and public sentiment, providing valuable insights into the media landscape.

cv selection