rollinglda

Construct Consistent Time Series from Textual Data

RollingLDA is a rolling version of the Latent Dirichlet Allocation. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.

Citation

Please cite the package using the BibTeX entry, which is obtained by the call citation("rollinglda").

References

• Rieger, J., Jentsch, C. & Rahnenführer, J. (2021). RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data. Accepted for Findings of EMNLP 2021.

Related Software

• tm is useful for preprocessing text data.
• lda offers a fast implementation of the Latent Dirichlet Allocation and is used by ldaPrototype.
• ldaPrototype offers a implementation of a model selection algorithm to increase the reliability of interpretations taken from LDA results and is used by rollinglda.
• quanteda is a framework for “Quantitative Analysis of Textual Data”.
• stm is a framework for Structural Topic Models.
• tosca is a framework for statistical methods in content analysis including visualizations and validation techniques. It is also useful for managing and manipulating text data to a structure requested by ldaPrototype and rollinglda.
• topicmodels is another framework for various topic models based on the Latent Dirichlet Allocation and Correlated Topics Models.
• (c)dtm is an implementation of dynamic topic models.
• Online LDA is an implementation of online learning for Latent Dirichlet Allocation.

Related Methods

• TM-LDA is an online modeling approach for latent topics (especially in social media).
• Streaming-LDA is a Copula-based approach to model document streams.
• Topics over Time is a continuous time model for word co-occurences.
• This paper presents a time-dependent topic model for multiple text streams.

Contribution

This R package is licensed under the GPLv3. For bug reports (lack of documentation, misleading or wrong documentation, unexpected behaviour, …) and feature requests please use the issue tracker. Pull requests are welcome and will be included at the discretion of the author.

Installation

#TBA install.packages("rollinglda")

For the development version use devtools:

devtools::install_github("JonasRieger/rollinglda")

(Quick Start) Example

TBA