as.data.frame.udpipe_connlu | Convert the result of udpipe_annotate to a tidy data frame |
as.matrix.cooccurrence | Convert the result of cooccurrence to a sparse matrix |
as_conllu | Convert a data.frame to CONLL-U format |
as_cooccurrence | Convert a matrix to a co-occurrence data.frame |
as_phrasemachine | Convert Parts of Speech tags to one-letter tags which can be used to identify phrases based on regular expressions |
as_word2vec | Convert a matrix of word vectors to word2vec format |
brussels_listings | Brussels AirBnB address locations available at www.insideairbnb.com |
brussels_reviews | Reviews of AirBnB customers on Brussels address locations available at www.insideairbnb.com |
brussels_reviews_anno | Reviews of the AirBnB customers which are tokenised, POS tagged and lemmatised |
cbind_dependencies | Add the dependency parsing information to an annotated dataset |
cbind_morphological | Add morphological features to an annotated dataset |
collocation | Extract collocations - a sequence of terms which follow each other |
cooccurrence | Create a cooccurence data.frame |
cooccurrence.character | Create a cooccurence data.frame |
cooccurrence.cooccurrence | Create a cooccurence data.frame |
cooccurrence.data.frame | Create a cooccurence data.frame |
document_term_frequencies | Aggregate a data.frame to the document/term level by calculating how many times a term occurs per document |
document_term_frequencies.character | Aggregate a data.frame to the document/term level by calculating how many times a term occurs per document |
document_term_frequencies.data.frame | Aggregate a data.frame to the document/term level by calculating how many times a term occurs per document |
document_term_frequencies_statistics | Add Term Frequency, Inverse Document Frequency and Okapi BM25 statistics to the output of document_term_frequencies |
document_term_matrix | Create a document/term matrix from a data.frame with 1 row per document/term |
document_term_matrix.data.frame | Create a document/term matrix from a data.frame with 1 row per document/term |
document_term_matrix.DocumentTermMatrix | Create a document/term matrix from a data.frame with 1 row per document/term |
document_term_matrix.simple_triplet_matrix | Create a document/term matrix from a data.frame with 1 row per document/term |
document_term_matrix.TermDocumentMatrix | Create a document/term matrix from a data.frame with 1 row per document/term |
dtm_bind | Combine 2 document term matrices either by rows or by columns |
dtm_cbind | Combine 2 document term matrices either by rows or by columns |
dtm_colsums | Column sums and Row sums for document term matrices |
dtm_cor | Pearson Correlation for Sparse Matrices |
dtm_rbind | Combine 2 document term matrices either by rows or by columns |
dtm_remove_lowfreq | Remove terms occurring with low frequency from a Document-Term-Matrix and documents with no terms |
dtm_remove_sparseterms | Remove terms with high sparsity from a Document-Term-Matrix |
dtm_remove_terms | Remove terms from a Document-Term-Matrix and keep only documents which have a least some terms |
dtm_remove_tfidf | Remove terms from a Document-Term-Matrix and documents with no terms based on the term frequency inverse document frequency |
dtm_reverse | Inverse operation of the document_term_matrix function |
dtm_rowsums | Column sums and Row sums for document term matrices |
dtm_tfidf | Term Frequency - Inverse Document Frequency calculation |
keywords_collocation | Extract collocations - a sequence of terms which follow each other |
keywords_phrases | Extract phrases - a sequence of terms which follow each other based on a sequence of Parts of Speech tags |
keywords_rake | Keyword identification using Rapid Automatic Keyword Extraction (RAKE) |
paste.data.frame | Concatenate text of each group of data together |
phrases | Extract phrases - a sequence of terms which follow each other based on a sequence of Parts of Speech tags |
predict.LDA | Predict method for an object of class LDA_VEM or class LDA_Gibbs |
predict.LDA_Gibbs | Predict method for an object of class LDA_VEM or class LDA_Gibbs |
predict.LDA_VEM | Predict method for an object of class LDA_VEM or class LDA_Gibbs |
strsplit.data.frame | Obtain a tokenised data frame by splitting text alongside a regular expression |
txt_collapse | Collapse a character vector while removing missing data. |
txt_contains | Check if text contains a certain pattern |
txt_freq | Frequency statistics of elements in a vector |
txt_highlight | Highlight words in a character vector |
txt_next | Get the n-th next element of a vector |
txt_nextgram | Based on a vector with a word sequence, get n-grams (looking forward) |
txt_previous | Get the n-th previous element of a vector |
txt_previousgram | Based on a vector with a word sequence, get n-grams (looking backward) |
txt_recode | Recode text to other categories |
txt_recode_ngram | Recode words with compound multi-word expressions |
txt_sample | Boilerplate function to sample one element from a vector. |
txt_sentiment | Perform dictionary-based sentiment analysis on a tokenised data frame |
txt_show | Boilerplate function to cat only 1 element of a character vector. |
txt_tagsequence | Identify a contiguous sequence of tags as 1 being entity |
udpipe | Tokenising, Lemmatising, Tagging and Dependency Parsing of raw text in TIF format |
udpipe_accuracy | Evaluate the accuracy of your UDPipe model on holdout data |
udpipe_annotate | Tokenising, Lemmatising, Tagging and Dependency Parsing Annotation of raw text |
udpipe_annotation_params | List with training options set by the UDPipe community when building models based on the Universal Dependencies data |
udpipe_download_model | Download an UDPipe model provided by the UDPipe community for a specific language of choice |
udpipe_load_model | Load an UDPipe model |
udpipe_read_conllu | Read in a CONLL-U file as a data.frame |
udpipe_train | Train a UDPipe model |
unique_identifier | Create a unique identifier for each combination of fields in a data frame |