Open Source Natural Language Processing Machine Learning models
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Updated
Dec 31, 2018 - Jupyter Notebook
Open Source Natural Language Processing Machine Learning models
Latent Semantic Analysis of Book Titles
Package for identifying the topics present in a collection of text documents and create summaries of texts
NLP Project for SDAIA T5 Data Science Bootcamp. This project consists of sentiment analysis for hotel reviews and classification algorithms based on that. Also, the project has word clustering models and a hotel recommendation system based on the nationalities and the reviewers' scores.
Latent Semantic Analysis applied on movies, both in a content-based approach (exploiting the movies overviews) and in a collaborative approach (exploiting the users rates)
This Python script utilizes NLTK and Scikit-learn to perform topic modeling on movie reviews using Latent Semantic Analysis. The output includes top topics and scores, word clouds for each topic, and the perplexity score of the LSA model.
An AI powered online content analysis tool. Web, api, ML Model, CICD, AWS
In this project we have tried to classify mails/messages as spam or ham
This repository provides an implementation of topic modelling techniques, namely Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA), specifically designed for analyzing news articles.
This is study repository. Win32, C#, C/C++.
Text Summarization
This is a repository implementing Latent Semantic Summarization from this paper and some form of abstractive summarization using this short guide.
A repository contains necessary foundational exercises in NLP for beginners.
The script gets a list of words from an excel sheet and will upload them to the following website: http://lsa.colorado.edu/cgi-bin/LSA-matrix.html, "This interface allows you to compare the similarity of multiple texts or terms within a particular LSA space. Each text is compared to all other texts." The results for each subject will be saved in…
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