easily bult LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT
Project description
easyLDA is a library that easily build LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT)
github: https://github.com/shichaoji/easyLDA
If you have a collection of documents, and what to explore the relationship & topics of the docs, easyLDA is a very handy library to use. Simply run the commend and you’ll get a trained LDA model with results visualized
The library pipeline text preprocessing, such as tf-idf, n-grams from Gensim library
Credit to:
installation
$ pip install easyLDA
usage example
simple need a text file (.csv) with each row represents a document (a post, comment, short article etc.), with only one column which is the text
text file (csv) sample view
easy to use, just in a shell window, type: easyLDA, then specify the location of the text document
1. then choose how many topics you want the model to fit
2. choose the topic contains only single word (1) or can be phases (2/3) as well
the program will be starting to train
in shell $ easyLDA
model result
models folder created by program contains the trained model
xx.html file is the interactive visulization of the model result
visualization live example
static pic result
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