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.
It is because every document has a unique document id.
require(BTM)
#> Loading required package: BTM
require(quanteda)
#> Loading required package: quanteda
#> Package version: 2.1.2
#> Parallel computing: 2 of 8 threads used.
#> See https://quanteda.io for tutorials and examples.
#>
#> Attaching package: 'quanteda'
#> The following object is masked from 'package:utils':
#>
#> View
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.
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)
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#> 2020-11-13 14:46:20 Start Gibbs sampling iteration 491/500This is how you should generate \(\theta_{t}\) . However, there are many NaN and there are only 1994 rows (trump2k has 2000 tweets) due to empty documents.
setdiff(docid(trump_corpus), row.names(theta))
#> [1] "text604" "text633" "text659" "text1586" "text1587" "text1761"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 has no problem generating word intrusion test for BTM like you do with other topic models.
oolong <- create_oolong(trump_btm)
oolong
#> An oolong test object with k = 8, 0 coded.
#> Use the method $do_word_intrusion_test() to do word intrusion test.
#> Use the method $lock() to finalize this object 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
#> An oolong test object with k = 8, 0 coded.
#> Use the method $do_word_intrusion_test() to do word intrusion test.
#> With 20 cases of topic intrusion test. 0 coded.
#> Use the method $do_topic_intrusion_test() to do topic intrusion test.
#> Use the method $lock() to finalize this object and see the results.btm_dataframe must not be NULL.
oolong <- create_oolong(trump_btm, trump_corpus)
#> Error in .extract_ingredients.input_model_s3_btm(.convert_input_model_s3(input_model), : You need to provide input_corpus (in quanteda format) and btm_dataframe for generating topic intrusion tests.input_corpus must be a quanteda corpus.
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