This repository contains an R package called topicmodels.etm which is an implementation of ETM
- ETM is a generative topic model combining traditional topic models (LDA) with word embeddings (word2vec)
- It models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic
- The model is fitted using an amortized variational inference algorithm on top of libtorch (https://torch.mlverse.org)
- The techniques are explained in detail in the paper: "Topic Modelling in Embedding Spaces" by Adji B. Dieng, Francisco J. R. Ruiz and David M. Blei, available at https://arxiv.org/pdf/1907.04907.pdf
- For installing the package from CRAN:
pkgs <- c("torch", "topicmodels.etm", "word2vec", "doc2vec", "udpipe", "uwot")
install.packages(pkgs)
library(torch)
library(topicmodels.etm)
- For installing the development version of this package: you can perform the following installations in R:
install.packages("torch")
install.packages("word2vec")
install.packages("doc2vec")
install.packages("udpipe")
install.packages("remotes")
library(torch)
remotes::install_github('bnosac/ETM', INSTALL_opts = '--no-multiarch')
- For allowing to plot the models:
install.packages("textplot")
install.packages("ggrepel")
install.packages("ggalt")
Build a topic model on questions answered in Belgian parliament in 2020 in Dutch.
- Example text of +/- 6000 questions asked in the Belgian parliament (available in R package doc2vec).
- Standardise the text a bit
library(torch)
library(topicmodels.etm)
library(doc2vec)
library(word2vec)
data(be_parliament_2020, package = "doc2vec")
x <- data.frame(doc_id = be_parliament_2020$doc_id,
text = be_parliament_2020$text_nl,
stringsAsFactors = FALSE)
x$text <- txt_clean_word2vec(x$text)
w2v <- word2vec(x = x$text, dim = 25, type = "skip-gram", iter = 10, min_count = 5, threads = 2)
embeddings <- as.matrix(w2v)
predict(w2v, newdata = c("migranten", "belastingen"), type = "nearest", top_n = 4)
$migranten
term1 term2 similarity rank
1 migranten lesbos 0.9434163 1
2 migranten chios 0.9334459 2
3 migranten vluchtelingenkampen 0.9269973 3
4 migranten kamp 0.9175452 4
$belastingen
term1 term2 similarity rank
1 belastingen belasting 0.9458982 1
2 belastingen ontvangsten 0.9091899 2
3 belastingen geheven 0.9071115 3
4 belastingen ontduiken 0.9029559 4
- Create a bag of words document term matrix (using the udpipe package but other R packages provide similar functionalities)
- Keep only the top 50% terms with the highest TFIDF
- Make sure document/term/matrix and the embedding matrix have the same vocabulary
library(udpipe)
dtm <- strsplit.data.frame(x, group = "doc_id", term = "text", split = " ")
dtm <- document_term_frequencies(dtm)
dtm <- document_term_matrix(dtm)
dtm <- dtm_remove_tfidf(dtm, prob = 0.50)
vocab <- intersect(rownames(embeddings), colnames(dtm))
embeddings <- dtm_conform(embeddings, rows = vocab)
dtm <- dtm_conform(dtm, columns = vocab)
dim(dtm)
dim(embeddings)
- Learn 20 topics with a 100-dimensional hyperparameter for the variational inference
set.seed(1234)
torch_manual_seed(4321)
model <- ETM(k = 20, dim = 100, embeddings = embeddings)
optimizer <- optim_adam(params = model$parameters, lr = 0.005, weight_decay = 0.0000012)
loss <- model$fit(data = dtm, optimizer = optimizer, epoch = 20, batch_size = 1000)
plot(model, type = "loss")
terminology <- predict(model, type = "terms", top_n = 5)
terminology
[[1]]
term beta
3891 zelfstandigen 0.05245856
2543 opdeling 0.02827548
5469 werkloosheid 0.02366866
3611 ocmw 0.01772762
4957 zelfstandige 0.01139760
[[2]]
term beta
3891 zelfstandigen 0.032309771
5469 werkloosheid 0.021119611
4957 zelfstandige 0.010217560
3611 ocmw 0.009712025
2543 opdeling 0.008961252
[[3]]
term beta
2537 gedetineerden 0.02914266
3827 nationaliteit 0.02540042
3079 gevangenis 0.02136421
5311 gevangenissen 0.01215335
3515 asielzoekers 0.01204639
[[4]]
term beta
3435 btw 0.02814350
5536 kostprijs 0.02012880
3508 pod 0.01218093
2762 vzw 0.01088356
2996 vennootschap 0.01015108
[[5]]
term beta
3372 verbaal 0.011172118
3264 politiezone 0.008422602
3546 arrondissement 0.007855867
3052 inbreuken 0.007204257
2543 opdeling 0.007149355
[[6]]
term beta
3296 instelling 0.04442037
3540 wetenschappelijke 0.03434755
2652 china 0.02702594
3043 volksrepubliek 0.01844959
3893 hongkong 0.01792639
[[7]]
term beta
2133 databank 0.003111386
3079 gevangenis 0.002650804
3255 dvz 0.002098217
3614 centra 0.001884672
2142 geneesmiddelen 0.001791468
[[8]]
term beta
2547 defensie 0.03706463
3785 kabinet 0.01323747
4054 griekse 0.01317877
3750 turkse 0.01238277
3076 leger 0.00964661
[[9]]
term beta
3649 nmbs 0.005472604
3704 beslag 0.004442090
2457 nucleaire 0.003911803
2461 mondmaskers 0.003712016
3533 materiaal 0.003513884
[[10]]
term beta
4586 politiezones 0.017413139
2248 voertuigen 0.012508971
3649 nmbs 0.008157282
2769 politieagenten 0.007591151
3863 beelden 0.006747020
[[11]]
term beta
3827 nationaliteit 0.009992087
4912 duitse 0.008966853
3484 turkije 0.008940011
2652 china 0.008723009
4008 overeenkomst 0.007879931
[[12]]
term beta
3651 opsplitsen 0.008752496
4247 kinderen 0.006497230
2606 sciensano 0.006430181
3170 tests 0.006420473
3587 studenten 0.006165542
[[13]]
term beta
3052 inbreuken 0.007657704
2447 drugs 0.006734609
2195 meldingen 0.005259825
3372 verbaal 0.005117311
3625 cyberaanvallen 0.004269334
[[14]]
term beta
2234 gebouwen 0.06128503
3531 digitale 0.03030998
3895 bpost 0.02974019
4105 regie 0.02608073
3224 infrabel 0.01758554
[[15]]
term beta
3649 nmbs 0.08117295
3826 station 0.03944306
3911 trein 0.03548101
4965 treinen 0.02843846
3117 stations 0.02732874
[[16]]
term beta
3649 nmbs 0.06778506
3240 personeelsleden 0.03363639
2972 telewerk 0.01857295
4965 treinen 0.01807373
3785 kabinet 0.01702784
[[17]]
term beta
2371 app 0.009092372
3265 stoffen 0.006641808
2461 mondmaskers 0.006462210
3025 persoonsgegevens 0.005374488
2319 websites 0.005372964
[[18]]
term beta
5296 aangifte 0.01940070
3435 btw 0.01360575
2762 vzw 0.01307520
2756 facturen 0.01233578
2658 rekenhof 0.01196285
[[19]]
term beta
3631 beperking 0.017481016
3069 handicap 0.010403863
3905 tewerkstelling 0.009714387
3785 kabinet 0.006984415
2600 ombudsman 0.006074827
[[20]]
term beta
3228 geweld 0.05881281
4178 vrouwen 0.05113553
4247 kinderen 0.04818219
2814 jongeren 0.01803746
2195 meldingen 0.01548613
newdata <- head(dtm, n = 5)
scores <- predict(model, newdata, type = "topics")
scores
torch_save(model, "example_etm.ckpt")
model <- torch_load("example_etm.ckpt")
Example plot shown above was created using the following code
- This uses R package textplot >= 0.2.0 which was updated on CRAN on 2021-08-18
- The summary function maps the learned embeddings of the words and topic centers in 2D using UMAP and textplot_embedding_2d plots the selected topics of interest in 2D
library(textplot)
library(uwot)
library(ggrepel)
library(ggalt)
manifolded <- summary(model, type = "umap", n_components = 2, metric = "cosine", n_neighbors = 15,
fast_sgd = FALSE, n_threads = 2, verbose = TRUE)
space <- subset(manifolded$embed_2d, type %in% "centers")
textplot_embedding_2d(space)
space <- subset(manifolded$embed_2d, cluster %in% c(12, 14, 9, 7) & rank <= 7)
textplot_embedding_2d(space, title = "ETM topics", subtitle = "embedded in 2D using UMAP",
encircle = FALSE, points = TRUE)
- Put embeddings of words and topic centers in 2D using UMAP
library(uwot)
centers <- as.matrix(model, type = "embedding", which = "topics")
embeddings <- as.matrix(model, type = "embedding", which = "words")
manifold <- umap(embeddings,
n_components = 2, metric = "cosine", n_neighbors = 15, fast_sgd = TRUE,
n_threads = 2, ret_model = TRUE, verbose = TRUE)
centers <- umap_transform(X = centers, model = manifold)
words <- manifold$embedding
- Plot words in 2D, color by topic and add topic centers in 2D
- This uses R package textplot >= 0.2.0 (https://github.com/bnosac/textplot) which was put on CRAN on 2021-08-18
library(data.table)
terminology <- predict(model, type = "terms", top_n = 7)
terminology <- rbindlist(terminology, idcol = "cluster")
df <- list(words = merge(x = terminology,
y = data.frame(x = words[, 1], y = words[, 2], term = rownames(embeddings)),
by = "term"),
centers = data.frame(x = centers[, 1], y = centers[, 2],
term = paste("Topic-", seq_len(nrow(centers)), sep = ""),
cluster = seq_len(nrow(centers))))
df <- rbindlist(df, use.names = TRUE, fill = TRUE, idcol = "type")
df <- df[, weight := ifelse(is.na(beta), 0.8, beta / max(beta, na.rm = TRUE)), by = list(cluster)]
library(textplot)
library(ggrepel)
library(ggalt)
x <- subset(df, type %in% c("words", "centers") & cluster %in% c(1, 3, 4, 8))
textplot_embedding_2d(x, title = "ETM topics", subtitle = "embedded in 2D using UMAP", encircle = FALSE, points = FALSE)
textplot_embedding_2d(x, title = "ETM topics", subtitle = "embedded in 2D using UMAP", encircle = TRUE, points = TRUE)
- Or if you like writing down the full ggplot2 code
library(ggplot2)
library(ggrepel)
x$topic <- factor(x$cluster)
plt <- ggplot(x,
aes(x = x, y = y, label = term, color = topic, cex = weight, pch = factor(type, levels = c("centers", "words")))) +
geom_text_repel(show.legend = FALSE) +
theme_void() +
labs(title = "ETM topics", subtitle = "embedded in 2D using UMAP")
plt + geom_point(show.legend = FALSE)
## encircle if topics are non-overlapping can provide nice visualisations
library(ggalt)
plt + geom_encircle(aes(group = topic, fill = topic), alpha = 0.4, show.legend = FALSE) + geom_point(show.legend = FALSE)
More examples are provided in the help of the ETM function see
?ETMDon't forget to set seeds to have reproducible behaviour
Need support in text mining? Contact BNOSAC: http://www.bnosac.be