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https://github.com/contefranz/OpTop

Optimal topic identification from a pool of Latent Dirichlet Allocation models
https://github.com/contefranz/OpTop

latent-dirichlet-allocation lda model-selection natural-language-processing nlp text-mining topic-modeling

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Optimal topic identification from a pool of Latent Dirichlet Allocation models

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# OpTop: detect the optimal number of topics from a pool of LDA models



## Overview



__OpTop__ is an `R` package that implements the testing approach described in 

the paper _A Statistical Approach for Optimal Topic Model Identification_ 

by Lewis and Grossetti (2019). 



Latent Dirichlet Allocation (LDA) was developed by Blei, Ng, and Jordan in 

2003 [Blei et al., (2003)] and is based on the idea that a corpus can be 

represented by a set of topics. LDA has been used extensively in computational 

linguistics, is replicable, and is automated so it cannot be influenced by 

researcher prejudice. LDA uses a likelihood approach to discover clusters of 

text, namely topics that frequently appear in a corpus.



One of the open challenges in topic modeling is to rigorously determine the 

optimal number of topics for a corpus. Extant research relies on heuristic 

approaches such as iterative trial-and-error procedures to select the number 

of topics. For example, a standard approach is to determine which specification 

is the least perplexed by the test sets. Perplexity is based on the intuition 

that a high degree of similarity, identified as a low level of perplexity, can 

be used to determine the appropriate number of topics [Blei et al., (2003); 

Hornik and Grün, (2011)].



__OpTop__ introduces a set of parametric tests to identify the optimal number of topics from a 

collection of LDA models. OpTop also includes several tests to explore topic stability and redundancy.



## Installation



The package is not on CRAN yet. You can install the development version as follows:

``` r

# Install the development version from Github:

devtools::install_github("contefranz/OpTop")

```



## Functions



All the procedures described in the paper will be implemented in this package.

The package is in beta stage and contains the following functions whose most of the internals 

are in `C++` and `C` to increase the performance.



* `get_topic_models()`: handy function to immediately get the list of topic models

the user wants to process from a specified environment;



* `optimal_topic()`: implements _Test 1_ of optimality from the methodological 

paper [Lewis and Grossetti (2019)].



* `topic_stability()`: implements _Test 2_ of topic stability from the 

methodological paper [Lewis and Grossetti (2019)].



* `agg_topic_stability()`: implements _Test 3_ of aggregate topic stability 

from the methodological paper [Lewis and Grossetti (2019)].



* `agg_document_stability()`: implements _Test 4_ of overall topic stability and

_Test 5_ of relative topic importance from the methodological paper 

[Lewis and Grossetti (2019)].



* `sim_dfm()`: convenient function to simulate a **quanteda** `dfm` object from a given 

LDA model of class `LDA_VEM` from **topicmodels**.



## Bug Reporting



Bugs and issues can be reported at

[https://github.com/contefranz/OpTop/issues](https://github.com/contefranz/OpTop/issues).



## Authors



* [Francesco Grossetti](http://faculty.unibocconi.eu/francescogiovannigrossetti/) 



  Assistant Professor of Data Science and Accounting Information Systems  

  Bocconi Institute for Data Science and Analytics ([BIDSA](https://www.bidsa.unibocconi.eu/wps/wcm/connect/Site/Bidsa/Home))  

  Accounting Department, Bocconi University.  

  Contact Francesco at: [email protected].  



* [Craig M. Lewis](https://business.vanderbilt.edu/bio/craig-lewis/)



  Madison S. Wigginton Professor of Finance  

  Owen Business School, Vanderbilt University.  

  Contact Craig at: [email protected].  



## Bibliography



1. Lewis, C. and Grossetti, F. (2022): _A Statistical Approach

for Optimal Topic Model Identification_ (forthcoming on Journal of Machine Learning Research)

2. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). _Latent Dirichlet Allocation_.

Journal of Machine Learning Research, 3(Jan):993–1022.

3. Benoit K., Watanabe K., Wang H., Nulty P., Obeng A., Müller S., Matsuo A.

(2018): _`quanteda`: An R package for the

quantitative analysis of textual data_. Journal of Open Source Software, 3(30), 774. doi: 10.21105/joss.00774

(URL: http://doi.org/10.21105/joss.00774), URL: https://quanteda.io)



***