topic.models: Uncover and assign topics to documents within a corpus
In JSmith146/CoRpEx: CorpEx is Designed for the Exploration and Mining of Large Textual Corpuses of Documents
Description
Usage
Arguments
Value
Examples
Description
This function utilizes Latent Dirichlet Allocation (LDA) and the
topicmodels package to allow a user to define a specified number of
k hidden topics. Once identified, each document's topic is assigned
to a new column in the dataset for further analysis.
Usage
1
topic.models(data.td, k = 2)
Arguments
data.td
A tidy dataset
k
User defined number of topics
Value
Attaches a new column to data.td identifying each document in a corpus as belonging to a specific topic
Examples
1
Articles<-topic.models(Articles, k=10)
JSmith146/CoRpEx documentation built on May 17, 2019, 10:11 p.m.
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Description Usage Arguments Value Examples
Description
This function utilizes Latent Dirichlet Allocation (LDA) and the
topicmodels package to allow a user to define a specified number of
k hidden topics. Once identified, each document's topic is assigned
to a new column in the dataset for further analysis.
Usage
1 | topic.models(data.td, k = 2)
|
Arguments
data.td |
A tidy dataset |
k |
User defined number of topics |
Value
Attaches a new column to data.td identifying each document in a corpus as belonging to a specific topic
Examples
1 | Articles<-topic.models(Articles, k=10)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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