,
María José Fernández Gómez [aut]
,
Susana Mendez [aut] | Title: | User-Friendly Interface for Review of Scientific Literature |
|---|---|
| Description: | Contains the development of a tool that provides a web-based graphical user interface (GUI) to perform a review of the scientific literature under the Bayesian approach of Latent Dirichlet Allocation (LDA)and machine learning algorithms. The application methodology is framed by the well known procedures in topic modelling on how to clean and process data. Contains methods described by Blei, David M., Andrew Y. Ng, and Michael I. Jordan (2003) <https://jmlr.org/papers/volume3/blei03a/blei03a.pdf> Allocation"; Thomas L. Griffiths and Mark Steyvers (2004) <doi:10.1073/pnas.0307752101> ; Xiong Hui, et al (2019) <doi:10.1016/j.cie.2019.06.010>. |
| Authors: | Javier De La Hoz Maestre [cre, aut]
,
María José Fernández Gómez [aut]
,
Susana Mendez [aut] |
| Maintainer: | Javier De La Hoz Maestre <[email protected]> |
| License: | GPL-3 |
| Version: | 0.9.3 |
| Built: | 2025-01-25 03:50:38 UTC |
| Source: | https://github.com/javierdelahoz/ldashiny |
This data set holds 20 news articles with additional
meta information from the Reuters-21578 data set. All documents belong
to the topic crude dealing with crude oil
data("crude")data("crude")
A VCorpus of 20 text documents. source Reuters-21578 Text Categorization Collection Distribution 1.0 XML format
Emms, Martin and Luz, Saturnino (2007). Machine Learning for Natural Language Processing. European Summer School of Logic, Language and Information, course reader.
# data("crude") # crude# data("crude") # crude
removeSparseTerms Remove Sparse Terms from a Term-Document Matrix function original pakage tm
removeSparseTerms(x, sparse)removeSparseTerms(x, sparse)
x |
A |
sparse |
A numeric for the maximal allowed sparsity in the range from bigger zero to smaller one. |
term-document matrix where those terms from x are removed
which have at least a sparse percentage of empty
(i.e., terms occurring 0 times in a document) elements. I.e.,
the resulting matrix contains only terms with a sparse factor
of less than sparse.
# data("crude") #library(tm) # tdm <- TermDocumentMatrix(crude) # removeSparseTerms(tdm, 0.3)# data("crude") #library(tm) # tdm <- TermDocumentMatrix(crude) # removeSparseTerms(tdm, 0.3)
Shiny UI for LDAShiny package
runLDAShiny(host = "127.0.0.1", port = NULL, launch.browser = TRUE)runLDAShiny(host = "127.0.0.1", port = NULL, launch.browser = TRUE)
host |
The IPv4 address that the application should listen on. Defaults to the shiny.host option, if set, or "127.0.0.1" if not. |
port |
is the TCP port that the application should listen on. If the port is not specified, and the shiny.port option is set (with options(shiny.port = XX)), then that port will be used. Otherwise, use a random port. |
launch.browser |
If true, the system's default web browser will be launched automatically after the app is started. Defaults to true in interactive sessions only. This value of this parameter can also be a function to call with the application's URL. |
# runLDAShiny()# runLDAShiny()