Installation

help(package="doc2vec")

Example on doc2vec

library(doc2vec)library(tokenizers.bpe)library(udpipe)data(belgium_parliament,package="tokenizers.bpe")x<- subset(belgium_parliament,language%in%"dutch")x<-data.frame(doc_id= sprintf("doc_%s",1:nrow(x)),text=x$text,stringsAsFactors=FALSE)x$text<- tolower(x$text)x$text<- gsub("[^[:alpha:]]","",x$text)x$text<- gsub("[[:space:]]+","",x$text)x$text<- trimws(x$text)x$nwords<- txt_count(x$text,pattern="")x<- subset(x,nwords<1000& nchar(text)>0)

## Low-dimensional model using DM, low number of iterations, for speed and display purposesmodel<- paragraph2vec(x=x,type="PV-DM",dim=5,iter=3,min_count=5,lr=0.05,threads=1)str(model)

## List of 3##  $ model  :<externalptr> ##  $ data   :List of 4##   ..$ file        : chr "C:\\Users\\Jan\\AppData\\Local\\Temp\\Rtmpk9Npjg\\textspace_1c446bffa0e.txt"##   ..$ n           : num 170469##   ..$ n_vocabulary: num 3867##   ..$ n_docs      : num 1000##  $ control:List of 9##   ..$ min_count: int 5##   ..$ dim      : int 5##   ..$ window   : int 5##   ..$ iter     : int 3##   ..$ lr       : num 0.05##   ..$ skipgram : logi FALSE##   ..$ hs       : int 0##   ..$ negative : int 5##   ..$ sample   : num 0.001##  - attr(*, "class")= chr "paragraph2vec_trained"

## More realistic modelmodel<- paragraph2vec(x=x,type="PV-DBOW",dim=100,iter=20,min_count=5,lr=0.05,threads=4)

embedding<- as.matrix(model,which="words")embedding<- as.matrix(model,which="docs")vocab<- summary(model,which="docs")vocab<- summary(model,which="words")

sentences<-list(sent1= c("geld","diabetes"),sent2= c("frankrijk","koning","proximus"))embedding<- predict(model,newdata=sentences,type="embedding")embedding<- predict(model,newdata= c("geld","koning"),type="embedding",which="words")embedding<- predict(model,newdata= c("doc_1","doc_10","doc_3"),type="embedding",which="docs")ncol(embedding)

## [1] 100

embedding[,1:4]

##              [,1]        [,2]       [,3]        [,4]## doc_1  0.05721277 -0.10298843  0.1089350 -0.03075439## doc_10 0.09553983  0.05211980 -0.0513489 -0.11847925## doc_3  0.08008177 -0.03324692  0.1563442  0.06585038

nn<- predict(model,newdata= c("proximus","koning"),type="nearest",which="word2word",top_n=5)nn

## [[1]]##      term1              term2 similarity rank## 1 proximus telefoontoestellen  0.5357178    1## 2 proximus            belfius  0.5169221    2## 3 proximus                ceo  0.4839031    3## 4 proximus            klanten  0.4819543    4## 5 proximus               taal  0.4590944    5## ## [[2]]##    term1          term2 similarity rank## 1 koning     ministerie  0.5615162    1## 2 koning verplaatsingen  0.5484987    2## 3 koning        familie  0.4911003    3## 4 koning       grondwet  0.4871097    4## 5 koning       gedragen  0.4694150    5

nn<- predict(model,newdata= c("proximus","koning"),type="nearest",which="word2doc",top_n=5)nn

## [[1]]##      term1   term2 similarity rank## 1 proximus doc_105  0.6684639    1## 2 proximus doc_863  0.5917463    2## 3 proximus doc_186  0.5233522    3## 4 proximus doc_620  0.4919243    4## 5 proximus doc_862  0.4619178    5## ## [[2]]##    term1   term2 similarity rank## 1 koning  doc_44  0.6686417    1## 2 koning  doc_45  0.5616031    2## 3 koning doc_583  0.5379452    3## 4 koning doc_943  0.4855201    4## 5 koning doc_797  0.4573555    5

nn<- predict(model,newdata= c("doc_198","doc_285"),type="nearest",which="doc2doc",top_n=5)nn

## [[1]]##     term1   term2 similarity rank## 1 doc_198 doc_343  0.5522854    1## 2 doc_198 doc_899  0.4902798    2## 3 doc_198 doc_983  0.4847047    3## 4 doc_198 doc_642  0.4829021    4## 5 doc_198 doc_336  0.4674844    5## ## [[2]]##     term1   term2 similarity rank## 1 doc_285 doc_319  0.5318567    1## 2 doc_285 doc_286  0.5100293    2## 3 doc_285 doc_113  0.5056069    3## 4 doc_285 doc_526  0.4840761    4## 5 doc_285 doc_488  0.4805686    5

sentences<-list(sent1= c("geld","frankrijk"),sent2= c("proximus","onderhandelen"))nn<- predict(model,newdata=sentences,type="nearest",which="sent2doc",top_n=5)nn

## $sent1##   term1   term2 similarity rank## 1 sent1 doc_742  0.4830917    1## 2 sent1 doc_151  0.4340138    2## 3 sent1 doc_825  0.4263285    3## 4 sent1 doc_740  0.4059283    4## 5 sent1 doc_776  0.4024554    5## ## $sent2##   term1   term2 similarity rank## 1 sent2 doc_105  0.5497447    1## 2 sent2 doc_863  0.5061581    2## 3 sent2 doc_862  0.4973840    3## 4 sent2 doc_620  0.4793786    4## 5 sent2 doc_186  0.4755909    5

sentences<- strsplit(setNames(x$text,x$doc_id),split="")nn<- predict(model,newdata=sentences,type="nearest",which="sent2doc",top_n=5)

Example on top2vec

library(doc2vec)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)model<- top2vec(d2v,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

Note

Support in text mining