| Type: | Package |
| Title: | Topic-Specific Diagnostics for LDA and CTM Topic Models |
| Version: | 0.1.1 |
| Description: | Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package. For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <doi:10.48550/arXiv.1206.4631>. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/doug-friedman/topicdoc |
| BugReports: | https://github.com/doug-friedman/topicdoc/issues |
| Depends: | R (≥ 3.5.0) |
| Imports: | slam, topicmodels |
| Suggests: | knitr, rmarkdown, stm, testthat (≥ 2.1.0) |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.0 |
| NeedsCompilation: | no |
| Packaged: | 2022-07-16 23:54:07 UTC; silly |
| Author: | Doug Friedman [aut, cre] |
| Maintainer: | Doug Friedman <doug.nhp@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-07-17 00:30:02 UTC |
Helper function for calculating coherence for a single topic's worth of terms
Description
Helper function for calculating coherence for a single topic's worth of terms
Usage
coherence(dtm_data, top_terms, smoothing_beta)
Arguments
dtm_data |
a document-term matrix of token counts coercible to |
top_terms |
a character vector of the top terms for a given topic |
smoothing_beta |
a numeric indicating the value to use to smooth the document frequencies in order avoid log zero issues, the default is 1 |
Value
a numeric indicating coherence for the topic
Helper function to check that a topic model and a dtm contain the same number of documents
Description
Helper function to check that a topic model and a dtm contain the same number of documents
Usage
contain_equal_docs(topic_model, dtm_data)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
dtm_data |
a document-term matrix of token counts coercible to |
Value
a logical indicating whether or not the two object contain the same number of documents
Calculate the distance of each topic from the overall corpus token distribution
Description
The Hellinger distance between the token probabilities or betas for each topic and the overall probability for the word in the corpus is calculated.
Usage
dist_from_corpus(topic_model, dtm_data)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
dtm_data |
a document-term matrix of token counts coercible to |
Value
A vector of distances with length equal to the number of topics in the fitted model
References
Jordan Boyd-Graber, David Mimno, and David Newman, 2014. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. CRC Handbooks ofModern Statistical Methods. CRC Press, Boca Raton, Florida.
Examples
# Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
dist_from_corpus(lda, AssociatedPress[1:20,])
Calculate the document prominence of each topic in a topic model
Description
Calculate the document prominence of each topic in a topic model based on either the number of documents with an estimated gamma probability above a threshold or the number of documents where a topic has the highest estimated gamma probability
Usage
doc_prominence(
topic_model,
method = c("gamma_threshold", "largest_gamma"),
gamma_threshold = 0.2
)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
method |
a string indicating which method to use - "gamma_threshold" or "largest_gamma", the default is "gamma_threshold" |
gamma_threshold |
a number between 0 and 1 indicating the gamma threshold to be used when using the gamma threshold method, the default is 0.2 |
Value
A vector of document prominences with length equal to the number of topics in the fitted model
References
Jordan Boyd-Graber, David Mimno, and David Newman, 2014. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. CRC Handbooks ofModern Statistical Methods. CRC Press, Boca Raton, Florida.
Examples
# Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
doc_prominence(lda)
Calculate the average token length for each topic in a topic model
Description
Using the the N highest probability tokens for each topic, calculate the average token length for each topic
Usage
mean_token_length(topic_model, top_n_tokens = 10)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
top_n_tokens |
an integer indicating the number of top words to consider, the default is 10 |
Value
A vector of average token lengths with length equal to the number of topics in the fitted model
References
Jordan Boyd-Graber, David Mimno, and David Newman, 2014. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. CRC Handbooks ofModern Statistical Methods. CRC Press, Boca Raton, Florida.
Examples
# Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
mean_token_length(lda)
Helper function to determine the number of topics in a topic model
Description
Helper function to determine the number of topics in a topic model
Usage
n_topics(topic_model)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
Value
an integer indicating the number of topics in the topic model
Calculate the distance between token and document frequencies
Description
Using the the N highest probability tokens for each topic, calculate the Hellinger distance between the token frequencies and the document frequencies
Usage
tf_df_dist(topic_model, dtm_data, top_n_tokens = 10)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
dtm_data |
a document-term matrix of token counts coercible to |
top_n_tokens |
an integer indicating the number of top words to consider, the default is 10 |
Value
A vector of distances with length equal to the number of topics in the fitted model
References
Jordan Boyd-Graber, David Mimno, and David Newman, 2014. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. CRC Handbooks ofModern Statistical Methods. CRC Press, Boca Raton, Florida.
Examples
# Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
tf_df_dist(lda, AssociatedPress[1:20,])
Helper function to calculate the Hellinger distance between the token frequencies and document frequencies for a specific topic's top N tokens
Description
Helper function to calculate the Hellinger distance between the token frequencies and document frequencies for a specific topic's top N tokens
Usage
tf_df_dist_diff(dtm_data, top_terms)
Arguments
dtm_data |
a document-term matrix of token counts coercible to |
top_terms |
- a character vector of the top N tokens |
Value
a single value representing the Hellinger distance
Calculate the topic coherence for each topic in a topic model
Description
Using the the N highest probability tokens for each topic, calculate the topic coherence for each topic
Usage
topic_coherence(topic_model, dtm_data, top_n_tokens = 10, smoothing_beta = 1)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
dtm_data |
a document-term matrix of token counts coercible to |
top_n_tokens |
an integer indicating the number of top words to consider, the default is 10 |
smoothing_beta |
a numeric indicating the value to use to smooth the document frequencies in order avoid log zero issues, the default is 1 |
Value
A vector of topic coherence scores with length equal to the number of topics in the fitted model
References
Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011, July). "Optimizing semantic coherence in topic models." In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 262-272). Association for Computational Linguistics. Chicago
McCallum, Andrew Kachites. "MALLET: A Machine Learning for Language Toolkit." https://mallet.cs.umass.edu 2002.
See Also
Examples
# Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
topic_coherence(lda, AssociatedPress[1:20,])
Calculate diagnostics for each topic in a topic model
Description
Generate a dataframe containing the diagnostics for each topic in a topic model
Usage
topic_diagnostics(
topic_model,
dtm_data,
top_n_tokens = 10,
method = c("gamma_threshold", "largest_gamma"),
gamma_threshold = 0.2
)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
dtm_data |
a document-term matrix of token counts coercible to |
top_n_tokens |
an integer indicating the number of top words to consider for mean token length |
method |
a string indicating which method to use - "gamma_threshold" or "largest_gamma" |
gamma_threshold |
a number between 0 and 1 indicating the gamma threshold to be used when using the gamma threshold method, the default is 0.2 |
Value
A dataframe where each row is a topic and each column contains the associated diagnostic values
References
Jordan Boyd-Graber, David Mimno, and David Newman, 2014. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. CRC Handbooks ofModern Statistical Methods. CRC Press, Boca Raton, Florida.
Examples
# Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
topic_diagnostics(lda, AssociatedPress[1:20,])
Calculate the exclusivity of each topic in a topic model
Description
Using the the N highest probability tokens for each topic, calculate the exclusivity for each topic
Usage
topic_exclusivity(topic_model, top_n_tokens = 10, excl_weight = 0.5)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
top_n_tokens |
an integer indicating the number of top words to consider, the default is 10 |
excl_weight |
a numeric between 0 and 1 indicating the weight to place on exclusivity versus frequency in the calculation, 0.5 is the default |
Value
A vector of exclusivity values with length equal to the number of topics in the fitted model
References
Bischof, Jonathan, and Edoardo Airoldi. 2012. "Summarizing topical content with word frequency and exclusivity." In Proceedings of the 29th International Conference on Machine Learning (ICML-12), eds John Langford and Joelle Pineau.New York, NY: Omnipress, 201–208.
See Also
Examples
# Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
topic_exclusivity(lda)
Calculate the size of each topic in a topic model
Description
Calculate the size of each topic in a topic model based on the number of fractional tokens found in each topic.
Usage
topic_size(topic_model)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
Value
A vector of topic sizes with length equal to the number of topics in the fitted model
References
Jordan Boyd-Graber, David Mimno, and David Newman, 2014. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. CRC Handbooks ofModern Statistical Methods. CRC Press, Boca Raton, Florida.
Examples
# Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
topic_size(lda)