Top FREX words from stm in tidy format

stm-frex.md

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

austen_sparse <- janeaustenr::austen_books() %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words) %>%
  count(book, word) %>%
  cast_sparse(book, word, n)
#> Joining, by = "word"

## train topic model
topic_model <- stm(austen_sparse, K = 12, init.type = "Spectral")
#> Beginning Spectral Initialization 
#>   Calculating the gram matrix...
#>   Using only 10000 most frequent terms during initialization...
#>   Finding anchor words...
#>      ............
#>   Recovering initialization...
#>      ....................................................................................................
#> Initialization complete.
#> ......
#> Completed E-Step (0 seconds). 
#> Completed M-Step. 
#> Completing Iteration 1 (approx. per word bound = -7.798) 
#> ......
#> Completed E-Step (0 seconds). 
#> Completed M-Step. 
#> Completing Iteration 2 (approx. per word bound = -7.693, relative change = 1.348e-02) 
#> ......
#> Completed E-Step (0 seconds). 
#> Completed M-Step. 
#> Model Converged

## compute FREX words
logbeta <- topic_model$beta$logbeta[[1]]
word_counts <- topic_model$settings$dim$wcounts$x
vocab <- topic_model$vocab
frex <- calcfrex(logbeta, 0.5, word_counts)

as_tibble(frex) %>%
  ## top 10 FREX words for each topic
  slice_head(n = 10) %>%
  ## reshape to tidy format
  mutate(rank = row_number()) %>%
  pivot_longer(V1:V12) %>%
  transmute(
    topic = str_replace(name, "V", "topic "),
    rank,
    word = vocab[value]
  ) %>%
  arrange(topic)
#> # A tibble: 120 × 3
#>    topic    rank word      
#>    <chr>   <int> <chr>     
#>  1 topic 1     1 elliot    
#>  2 topic 1     2 wentworth 
#>  3 topic 1     3 walter    
#>  4 topic 1     4 anne      
#>  5 topic 1     5 russell   
#>  6 topic 1     6 musgrove  
#>  7 topic 1     7 louisa    
#>  8 topic 1     8 charles   
#>  9 topic 1     9 uppercross
#> 10 topic 1    10 kellynch  
#> # … with 110 more rows
#> # ℹ Use `print(n = ...)` to see more rows

^{Created on 2022-08-09 by the reprex package (v2.0.1)}