LDAvis | R package for web-based interactive topic model | Topic Modeling library

by cpsievert JavaScript Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | LDAvis Summary

LDAvis is a JavaScript library typically used in Artificial Intelligence, Topic Modeling applications. LDAvis has no bugs, it has no vulnerabilities and it has low support. However LDAvis has a Non-SPDX License. You can download it from GitHub.

R package for interactive topic model visualization. LDAvis is designed to help users interpret the topics in a topic model that has been fit to a corpus of text data. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization.

Support

Quality

Security

License

Reuse

Support

LDAvis has a low active ecosystem.

It has 532 star(s) with 131 fork(s). There are 31 watchers for this library.

It had no major release in the last 6 months.

There are 31 open issues and 48 have been closed. On average issues are closed in 213 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of LDAvis is current.

Quality

LDAvis has 0 bugs and 0 code smells.

Security

LDAvis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

LDAvis code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

LDAvis has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

LDAvis releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

It has 965 lines of code, 0 functions and 25 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of LDAvis

Get all kandi verified functions for this library.

LDAvis Key Features

No Key Features are available at this moment for LDAvis.

LDAvis Examples and Code Snippets

No Code Snippets are available at this moment for LDAvis.

Community Discussions

Trending Discussions on LDAvis

Count the number of tokens in a Documenttermmatrix

pyLDAvis .show function asks for missing .css file in Jupyter notebook

QUESTION

Count the number of tokens in a Documenttermmatrix

Asked 2021-Jun-21 at 13:17

I have a question to a Documenttermmatrix. I would like to use the "LDAVIS" package in R. To visualize my results of the LDA algorithm I need to calculate the number of tokens of every document. I don´t have the text corpus for the considered DTM. Does anyone know how I can calculate the amount of tokens for every Document. The output as a list with the document name and his amount of tokens would be the perfect solution.

Kind Regards, Tom

...

ANSWER

Answered 2021-Jun-21 at 13:17

You can use slam::row_sums. This calculates the row_sums of a document term matrix without first transforming the dtm into a matrix. This function comes from the slam package which is installed when you install the tm package.

Source https://stackoverflow.com/questions/68067760

QUESTION

pyLDAvis .show function asks for missing .css file in Jupyter notebook

Asked 2021-Feb-26 at 22:40

import pyLDAvis
import pyLDAvis.gensim
pyLDAvis.enable_notebook()
LDAvis_prepared = pyLDAvis.gensim.prepare(lda_model, bow_corpus, dictionary)
pyLDAvis.show(LDAvis_prepared)

...

ANSWER

Answered 2021-Feb-17 at 07:13

Try to specify the version of pyLDAvis to 2.1.2

Source https://stackoverflow.com/questions/66080712

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install LDAvis

Once installed, we recommend a visit to the main help page:.

Support

To read about the methodology behind LDAvis, see our paper, which we presented at the 2014 ACL Workshop on Interactive Language Learning, Visualization, and Interfaces in Baltimore on June 27, 2014.

Find more information at: