The package BTM by Jan Wijffels et al. finds “topics in collections of short text”. Compared to other topic model packages, BTM requires a special data format for training. Oolong has no problem generating word intrusion tests with BTM. However, that special data format can make creation of topic intrusion tests very tricky.

This guide provides our recommendations on how to use BTM, so that the model can be used for generating topic intrusion tests.

Requirement #1: Keep your quanteda corpus

It is because every document has a unique document id.

require(BTM)
#> Loading required package: BTM
require(quanteda)
#> Loading required package: quanteda
#> Package version: 4.1.0
#> Unicode version: 15.1
#> ICU version: 74.2
#> Parallel computing: disabled
#> See https://quanteda.io for tutorials and examples.
require(oolong)
#> Loading required package: oolong
trump_corpus <- corpus(trump2k)

And then you can do regular text cleaning, stemming procedure with quanteda. Instead of making the product a DFM object, make it a token object. You may read this issue by Benoit et al.

tokens(trump_corpus, remove_punct = TRUE, remove_numbers = TRUE, remove_symbols = TRUE, split_hyphens = TRUE, remove_url = TRUE) %>% tokens_tolower() %>% tokens_remove(stopwords("en")) %>% tokens_remove("@*")  -> trump_toks

Requirement #2: Keep your data frame

Use this function to convert the token object to a data frame.

as.data.frame.tokens <- function(x) {
  data.frame(
    doc_id = rep(names(x), lengths(x)),
    tokens = unlist(x, use.names = FALSE)
  )
}

trump_dat <- as.data.frame.tokens(trump_toks)

Train a BTM model

trump_btm <- BTM(trump_dat, k = 8, iter = 500, trace = 10)

Pecularities of BTM

This is how you should generate θ_t . However, there are many NaN and there are only 1994 rows (trump2k has 2000 tweets) due to empty documents.

theta <- predict(trump_btm, newdata = trump_dat)
dim(theta)
#> [1] 1994    8

setdiff(docid(trump_corpus), row.names(theta))
#> [1] "text604"  "text633"  "text659"  "text1586" "text1587" "text1761"

trump_corpus[604]
#> Corpus consisting of 1 document.
#> text604 :
#> "http://t.co/PtViAyrO4A"

Also, the row order is messed up.

head(row.names(theta), 100)
#>   [1] "text1"    "text10"   "text100"  "text1000" "text1001" "text1002"
#>   [7] "text1003" "text1004" "text1005" "text1006" "text1007" "text1008"
#>  [13] "text1009" "text101"  "text1010" "text1011" "text1012" "text1013"
#>  [19] "text1014" "text1015" "text1016" "text1017" "text1018" "text1019"
#>  [25] "text102"  "text1020" "text1021" "text1022" "text1023" "text1024"
#>  [31] "text1025" "text1026" "text1027" "text1028" "text1029" "text103" 
#>  [37] "text1030" "text1031" "text1032" "text1033" "text1034" "text1035"
#>  [43] "text1036" "text1037" "text1038" "text1039" "text104"  "text1040"
#>  [49] "text1041" "text1042" "text1043" "text1044" "text1045" "text1046"
#>  [55] "text1047" "text1048" "text1049" "text105"  "text1050" "text1051"
#>  [61] "text1052" "text1053" "text1054" "text1055" "text1056" "text1057"
#>  [67] "text1058" "text1059" "text106"  "text1060" "text1061" "text1062"
#>  [73] "text1063" "text1064" "text1065" "text1066" "text1067" "text1068"
#>  [79] "text1069" "text107"  "text1070" "text1071" "text1072" "text1073"
#>  [85] "text1074" "text1075" "text1076" "text1077" "text1078" "text1079"
#>  [91] "text108"  "text1080" "text1081" "text1082" "text1083" "text1084"
#>  [97] "text1085" "text1086" "text1087" "text1088"

Oolong’s support for BTM

Oolong has no problem generating word intrusion test for BTM like you do with other topic models.

oolong <- create_oolong(trump_btm)
oolong
#> 
#> ── oolong (topic model) ────────────────────────────────────────────────────────
#> ✔ WI ✖ TI ✖ WSI
#> ℹ WI: k = 8, 0 coded.
#> 
#> ── Methods ──
#> 
#> • <$do_word_intrusion_test()>: do word intrusion test
#> • <$lock()>: finalize and see the results

For generating topic intrusion tests, however, you must provide the data frame you used for training (in this case trump_dat). Your input_corpus must be a quanteda corpus too.

oolong <- create_oolong(trump_btm, trump_corpus, btm_dataframe = trump_dat)
oolong
#> 
#> ── oolong (topic model) ────────────────────────────────────────────────────────
#> ✔ WI ✔ TI ✖ WSI
#> ℹ WI: k = 8, 0 coded.
#> ℹ TI: n = 20, 0 coded.
#> 
#> ── Methods ──
#> 
#> • <$do_word_intrusion_test()>: do word intrusion test
#> • <$do_topic_intrusion_test()>: do topic intrusion test
#> • <$lock()>: finalize and see the results

btm_dataframe must not be NULL.

oolong <- create_oolong(trump_btm, trump_corpus)
#> Error: You need to provide input_corpus (in quanteda format) and btm_dataframe for generating topic intrusion tests.

input_corpus must be a quanteda corpus.

oolong <- create_oolong(trump_btm, trump2k, btm_dataframe = trump_dat)
#> Error: You need to provide input_corpus (in quanteda format) and btm_dataframe for generating topic intrusion tests.

GESIS↩︎