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.

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

userfriendly bot normally interacts as friend

Improve access to healthcare services and reduce costs.

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.🌟

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

Twitter Sentiment Analysis

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

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

Applying Word 2 Vec Algorithm on Reddit comments

Exploratory Data Analysis on various data set.

Latent Dirichlet allocation

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

cv selection

Streamlit app for topic modeling (NMF & LDA) on news articles — TF-IDF, Gensim, similar-article lookup, pyLDAvis.

🚀 What we built: An AI-powered Women’s Safety & Well-Being Detector — a web app that flags multiple forms of online abuse in real time and offers tools for emotional recovery. 📊 Under the hood: BiLSTM + Word2Vec embeddings for deep, context-aware detection Trained on 21K+ labeled comments across 7 toxicity categories Built with Python, Tensor

This project applies Latent Dirichlet Allocation (LDA) for topic modeling and Singular Value Decomposition (SVD) for dimensionality reduction to uncover patterns in text data, focusing on document clustering and latent semantic analysis.