LDA topic modeling C++ software

Implementation of Few Unsupervised Clustering ALgorithm

Generating topics for any given news report using LDA

I used transcripts of television episodes to look for patterns between shows using the Latent Dirichlet Allocation (LDA) model, which clusters similar shows based upon the two shows use similar words at similar frequencies.

Unsupervised machine learning exploration of NBA topics on Twitter.

LDA visualization

Query and preprocess more than 1 M data from MongoDB(NoSQL). Jobs classification and analysis to define skills demanded by the job market trend with unsupervised NLP and data visualization.

Generating shorter a version of the input text, preserving all the necessary content.

Classify, find categories for, and automatically process complaint emails

Building a recommendation system for Google Play games using content-based and collaborative filtering models

Exploratory and algorithmic (LDA, Supervised LDA, Regression) analysis of video game reviews

Different Analytics about sarcasm on posts from Reddit using NLP techniques

Cythonized implementation of Topic modeling.

In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.

Provides support for making predictions of binary outcomes based on natural language corpora. Formally, creates predictive models using Logistic Regression with Elastic Net Regularization on topic models derived from Latent Dirichlet Allocation.

This project presents an overview of Topic Modelling - a classical problem of unsupervised machine learning’s branch i.e., Natural Language Processing (NLP) - by studying and comparing two latent algorithms - Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). These techniques are applied to a public dataset - ‘A Million News H…

Topic modeling of Indonesian PER literatures using latent Dirichlet allocation (LDA)

Inference in the Bayesian Latent Dirichlet Allocation (LDA) using Gibbs Sampling and Variational Bayes

Projet d'introduction au NLP et à la COMPUTER VISION

My version of topic modelling using Latent Dirichlet Allocation (LDA) which finds the best number of topics for a set of documents using ldatuning package which comes with different metrics