Other packages > Find by keyword >

doc2vec  

Distributed Representations of Sentences, Documents and Topics
View on CRAN: Click here


Download and install doc2vec package within the R console
Install from CRAN:
install.packages("doc2vec")

Install from Github:
library("remotes")
install_github("cran/doc2vec")

Install by package version:
library("remotes")
install_version("doc2vec", "0.2.2")



Attach the package and use:
library("doc2vec")
Maintained by
Jan Wijffels
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-12-10
Latest Update: 2021-03-28
Description:
Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms, namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at . The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering. It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps these document embeddings to a lower-dimensional space using the 'Uniform Manifold Approximation and Projection' (UMAP) clustering algorithm and finds dense areas in that space using a 'Hierarchical Density-Based Clustering' technique (HDBSCAN). These dense areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can be found which are representative of the topic. More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at .
How to cite:
Jan Wijffels (2020). doc2vec: Distributed Representations of Sentences, Documents and Topics. R package version 0.2.2, https://cran.r-project.org/web/packages/doc2vec. Accessed 18 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:33), 0.1.0 (2020-12-10 10:00), 0.1.1 (2021-01-21 18:20), 0.2.0 (2021-03-28 01:00)
Other packages that cited doc2vec R package
View doc2vec citation profile
Other R packages that doc2vec depends, imports, suggests or enhances
Complete documentation for doc2vec
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

eyelinker  
Import ASC Files from EyeLink Eye Trackers
Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
downlit  
Syntax Highlighting and Automatic Linking
Syntax highlighting of R code, specifically designed for the needs of 'RMarkdown' packages like 'pk ...
Download / Learn more Package Citations See dependency  
hmeasure  
The H-Measure and Other Scalar Classification Performance Metrics
Classification performance metrics that are derived from the ROC curve of a classifier. The package ...
Download / Learn more Package Citations See dependency  
data360r  
Wrapper for 'TCdata360' and 'Govdata360' API
Makes it easy to engage with the Application Program Interface (API) of the 'TCdata360' and 'Govdat ...
Download / Learn more Package Citations See dependency  
injectoR  
R Dependency Injection
R dependency injection framework. Dependency injection allows a program design to follow the depend ...
Download / Learn more Package Citations See dependency  

28,565

R Packages

239,283

Dependencies

75,677

Author Associations

28,566

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA