# Distributed Representations of Topics Perform text clustering by using semantic embeddings of documents and words to find topics of text documents which are semantically similar. ```r top2vec( x, data = data.frame(doc_id = character(), text = character(), stringsAsFactors = FALSE), control.umap = list(n_neighbors = 15L, n_components = 5L, metric = "cosine"), control.dbscan = list(minPts = 100L), control.doc2vec = list(), umap = uwot::umap, trace = FALSE, ... ) ``` ## Arguments - `x`: either an object returned by `paragraph2vec` or a data.frame with columns `doc_id` and `text` storing document ids and texts as character vectors or a matrix with document embeddings to cluster or a list with elements docs and words containing document embeddings to cluster and word embeddings for deriving topic summaries - `data`: optionally, a data.frame with columns `doc_id` and `text` representing documents. This dataset is just stored, in order to extract the text of the most similar documents to a topic. If it also contains a field `text_doc2vec`, this will be used to indicate the most relevant topic words by class-based tfidf - `control.umap`: a list of arguments to pass on to `umap` for reducing the dimensionality of the embedding space - `control.dbscan`: a list of arguments to pass on to `hdbscan` for clustering the reduced embedding space - `control.doc2vec`: optionally, a list of arguments to pass on to `paragraph2vec` in case `x` is a data.frame instead of a doc2vec model trained by `paragraph2vec` - `umap`: function to apply UMAP. Defaults to `umap`, can as well be `tumap` - `trace`: logical indicating to print evolution of the algorithm - `...`: further arguments not used yet ## Returns an object of class `top2vec` which is a list with elements * embedding: a list of matrices with word and document embeddings * doc2vec: a doc2vec model * umap: a matrix of representations of the documents of `x` * dbscan: the result of the hdbscan clustering * data: a data.frame with columns doc_id and text * size: a vector of frequency statistics of topic occurrence * k: the number of clusters * control: a list of control arguments to doc2vec / umap / dbscan ## Note The topic '0' is the noise topic ## Examples ```r library(word2vec) library(uwot) library(dbscan) 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) x <- subset(x, txt_count_words(text) < 1000) d2v <- paragraph2vec(x, type = "PV-DBOW", dim = 50, lr = 0.05, iter = 10, window = 15, hs = TRUE, negative = 0, sample = 0.00001, min_count = 5, threads = 1) # write.paragraph2vec(d2v, "d2v.bin") # d2v <- read.paragraph2vec("d2v.bin") model <- top2vec(d2v, data = x, control.dbscan = list(minPts = 50), control.umap = list(n_neighbors = 15L, n_components = 4), trace = TRUE) model <- top2vec(d2v, data = x, control.dbscan = list(minPts = 50), control.umap = list(n_neighbors = 15L, n_components = 3), umap = tumap, trace = TRUE) info <- summary(model, top_n = 7) info$topwords info$topdocs library(udpipe) info <- summary(model, top_n = 7, type = "c-tfidf") info$topwords ## Change the model: reduce doc2vec model to 2D model <- update(model, type = "umap", n_neighbors = 100, n_components = 2, metric = "cosine", umap = tumap, trace = TRUE) info <- summary(model, top_n = 7) data = x info$topwords info$topdocs ## Change the model: have minimum 200 points for the core elements in the hdbscan density model <- update(model, type = "hdbscan", minPts = 200, trace = TRUE) info <- summary(model, top_n = 7) data = x info$topwords info$topdocs ## ## Example on a small sample ## with unrealistic hyperparameter settings especially regarding dim / iter / n_epochs ## in order to have a basic example finishing < 5 secs ## library(uwot) library(dbscan) library(word2vec) data(be_parliament_2020, package = "doc2vec") x <- data.frame(doc_id = be_parliament_2020$doc_id, text = be_parliament_2020$text_nl, stringsAsFactors = FALSE) x <- head(x, 1000) x$text <- txt_clean_word2vec(x$text) x <- subset(x, txt_count_words(text) < 1000) d2v <- paragraph2vec(x, type = "PV-DBOW", dim = 10, lr = 0.05, iter = 0, window = 5, hs = TRUE, negative = 0, sample = 0.00001, min_count = 5) emb <- list(docs = as.matrix(d2v, which = "docs"), words = as.matrix(d2v, which = "words")) model <- top2vec(emb, data = x, control.dbscan = list(minPts = 50), control.umap = list(n_neighbors = 15, n_components = 2, init = "spectral"), umap = tumap, trace = TRUE) info <- summary(model, top_n = 7) print(info, top_n = c(5, 2)) ``` ## References [https://arxiv.org/abs/2008.09470](https://arxiv.org/abs/2008.09470) ## See Also `paragraph2vec`

top2vec function