Tidy topic models fit by the stm package. The arguments and return values are similar to lda_tidiers().

# S3 method for STM
tidy(
  x,
  matrix = c("beta", "gamma", "theta", "frex", "lift"),
  log = FALSE,
  document_names = NULL,
  ...
)

# S3 method for estimateEffect
tidy(x, ...)

# S3 method for estimateEffect
glance(x, ...)

# S3 method for STM
augment(x, data, ...)

# S3 method for STM
glance(x, ...)

Arguments

x

An STM fitted model object from either stm::stm() or stm::estimateEffect()

matrix

Which matrix to tidy:

  • the beta matrix (per-term-per-topic, default)

  • the gamma/theta matrix (per-document-per-topic); the stm package calls this the theta matrix, but other topic modeling packages call this gamma

  • the FREX matrix, for words with high frequency and exclusivity

  • the lift matrix, for words with high lift

log

Whether beta/gamma/theta should be on a log scale, default FALSE

document_names

Optional vector of document names for use with per-document-per-topic tidying

...

Extra arguments for tidying, such as w as used in stm::calcfrex()

data

For augment, the data given to the stm function, either as a dfm from quanteda or as a tidied table with "document" and "term" columns

Value

tidy returns a tidied version of either the beta, gamma, FREX, or lift matrix if called on an object from stm::stm(), or a tidied version of the estimated regressions if called on an object from stm::estimateEffect().

glance returns a tibble with exactly one row of model summaries.

augment must be provided a data argument, either a dfm from quanteda or a table containing one row per original document-term pair, such as is returned by tdm_tidiers, containing columns document and term. It returns that same data with an additional column .topic with the topic assignment for that document-term combination.

Examples

library(dplyr)
library(ggplot2)
library(stm)
#> stm v1.3.6 successfully loaded. See ?stm for help. 
#>  Papers, resources, and other materials at structuraltopicmodel.com
library(janeaustenr)

austen_sparse <- austen_books() %>%
    unnest_tokens(word, text) %>%
    anti_join(stop_words) %>%
    count(book, word) %>%
    cast_sparse(book, word, n)
#> Joining, by = "word"
topic_model <- stm(austen_sparse, K = 12, verbose = FALSE)

# tidy the word-topic combinations
td_beta <- tidy(topic_model)
td_beta
#> # A tibble: 166,968 × 3
#>    topic term      beta
#>    <int> <chr>    <dbl>
#>  1     1 1     1.18e- 4
#>  2     2 1     1.15e-19
#>  3     3 1     5.51e- 5
#>  4     4 1     1.33e-19
#>  5     5 1     4.20e- 5
#>  6     6 1     2.68e- 5
#>  7     7 1     4.20e- 5
#>  8     8 1     1.18e- 4
#>  9     9 1     4.20e- 5
#> 10    10 1     4.20e- 5
#> # … with 166,958 more rows

# Examine the topics
td_beta %>%
    group_by(topic) %>%
    slice_max(beta, n = 10) %>%
    ungroup() %>%
    ggplot(aes(beta, term)) +
    geom_col() +
    facet_wrap(~ topic, scales = "free")


# high FREX words per topic
tidy(topic_model, matrix = "frex")
#> # A tibble: 166,968 × 2
#>    topic term       
#>    <int> <chr>      
#>  1     1 weak       
#>  2     1 fourteen   
#>  3     1 inferiority
#>  4     1 person's   
#>  5     1 assertion  
#>  6     1 hearty     
#>  7     1 gardens    
#>  8     1 dropped    
#>  9     1 bench      
#> 10     1 negligence 
#> # … with 166,958 more rows

# high lift words per topic
tidy(topic_model, matrix = "lift")
#> # A tibble: 166,968 × 2
#>    topic term           
#>    <int> <chr>          
#>  1     1 acknowledgement
#>  2     1 lyme           
#>  3     1 benwick        
#>  4     1 henrietta      
#>  5     1 kellynch       
#>  6     1 musgrove       
#>  7     1 walter         
#>  8     1 russell        
#>  9     1 uppercross     
#> 10     1 wentworth      
#> # … with 166,958 more rows

# tidy the document-topic combinations, with optional document names
td_gamma <- tidy(topic_model, matrix = "gamma",
                 document_names = rownames(austen_sparse))
td_gamma
#> # A tibble: 72 × 3
#>    document            topic      gamma
#>    <chr>               <int>      <dbl>
#>  1 Sense & Sensibility     1 0.00000260
#>  2 Pride & Prejudice       1 0.00000279
#>  3 Mansfield Park          1 0.00000268
#>  4 Emma                    1 0.00000230
#>  5 Northanger Abbey        1 0.00000761
#>  6 Persuasion              1 0.333     
#>  7 Sense & Sensibility     2 0.00000472
#>  8 Pride & Prejudice       2 0.00000483
#>  9 Mansfield Park          2 0.00000393
#> 10 Emma                    2 1.00      
#> # … with 62 more rows

# using stm's gardarianFit, we can tidy the result of a model
# estimated with covariates
effects <- estimateEffect(1:3 ~ treatment, gadarianFit, gadarian)
glance(effects)
#> # A tibble: 1 × 3
#>       k  docs uncertainty
#>   <int> <int> <chr>      
#> 1     3   341 Global     
td_estimate <- tidy(effects)
td_estimate
#> # A tibble: 6 × 6
#>   topic term        estimate std.error statistic  p.value
#>   <int> <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1     1 (Intercept)   0.437     0.0241     18.1  1.13e-51
#> 2     1 treatment    -0.153     0.0322     -4.75 3.00e- 6
#> 3     2 (Intercept)   0.212     0.0224      9.44 6.18e-19
#> 4     2 treatment     0.242     0.0331      7.31 1.93e-12
#> 5     3 (Intercept)   0.351     0.0231     15.2  2.57e-40
#> 6     3 treatment    -0.0892    0.0320     -2.79 5.58e- 3