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Xiaoting Kuang's Projects

textgenrnn icon textgenrnn

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

textminer icon textminer

An aid for text mining in R, with a syntax that should be familiar to experienced R users. Provides a wrapper for several topic models that take similarly-formatted input and give similarly-formatted output. Has additional functionality for analyzing and diagnostics for topic models.

textplot icon textplot

(Mental) maps of texts with kernel density estimation and force-directed networks.

textsum icon textsum

Preparing a dataset for TensorFlow text summarization (TextSum) model.

textsum-1 icon textsum-1

Modifications on Tensorflow's textsum (https://github.com/tensorflow/models/tree/master/textsum)

tf_convwta icon tf_convwta

Tensorflow implementation of convolutional Winner-Take-All Autoencdoer

tidy-text-mining icon tidy-text-mining

Manuscript of the book "Tidy Text Mining with R" by Julia Silge and David Robinson

tidytext icon tidytext

Text mining using dplyr, ggplot2, and other tidy tools :sparkles::page_facing_up::sparkles::page_facing_up::sparkles:

tikz-bayesnet icon tikz-bayesnet

TikZ library for drawing Bayesian networks, graphical models and (directed) factor graphs in LaTeX.

topic-modelling-on-wiki-corpus icon topic-modelling-on-wiki-corpus

It uses Latent Dirichlet Allocation algorithm to discover hidden topics from the articles. It is trained on 60,000 articles taken from simple wikipedia english corpus. Finally, It can extract the topic of the given input text article.

topicapp icon topicapp

A simple Shiny App for Topic Modeling in R

topicmodelvis icon topicmodelvis

A Django project to build an interactive visualisation tool for topic models

traceminer icon traceminer

Utility to mine an Oracle 10046 Trace file, with Binds, and list the SQL statements with binds replaced by actual values used at EXEC time.

twitter-sentiment-analysis icon twitter-sentiment-analysis

- Scrapped data from twitter using selenium for Hillary Clinton and Donald Trump - Cleaned the data using Re and Nltk and performed various classification algorithms - Using GridSearch compared RF, KNN, MLP, SVM algorithms and found out the best efficient algorithm - Predicted the feature words using vectorizer which were most efficient

twitter-stream-ml icon twitter-stream-ml

Machine Learning over Twitter's stream. Using Apache Spark, Web Server and Lightning Graph server.

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