tm

I am facing the below error while working on the TM package with R. library("tm") Loading required package: NLP Warning messages: 1: package ‘tm’ was built under R version 3.4.2 2: package ‘NLP’ was built under R version 3.4.1 corpus <- VCorpus(DataframeSource(data)) Error: all(!is.na(match(c("doc_id", "text"), names(x)))) is not TRUE Have tried various ways like reinstalling the package, updating with new version of R but the error still persists. For the same data file the same code runs on another system with the same version of R. Eva I met the same problem when I updated the tm package to

I am trying to figure out a way to calculate word proximities to a specific term in a document as well as the average proximity (by word). I know there are similar questions on SO, but nothing that gives me the answer I need or even points me somewhere helpful. So let's say I have the following text: song <- "Far over the misty mountains cold To dungeons deep and caverns old We must away ere break of day To seek the pale enchanted gold. The dwarves of yore made mighty spells, While hammers fell like ringing bells In places deep, where dark things sleep, In hollow halls beneath the fells. For

I have a corpus containing journal data of 15 observations of 3 variables (ID, title, abstract). Using R Studio I read in the data from a .csv file (one line per observation). When performing some text mining operations I got some trouble when using the method stemCompletion. After applying stemCompletion I observed that the results are provided for each stemmed line of the .csv three times. All the other tm methods (e.g. stemDocument) produce only a single result. I'm wondering why this happens and how I could fix the problem I used the code below: data.corpus <- Corpus(DataframeSource(data))

It's straightforward to build a document-term matrix from a corpus with the tm package. I'd like to build a corpus from a document-term-matrix. Let M be the number of documents in a document set. Let V be the number of terms in the vocabulary of that document set.Then a document-term-matrix is an M*V matrix. I also have a vocabulary vector, of length V. In the vocabulary vector are the words represented by indices in the document-term-matrix. From the dtm and vocabulary vector, I'd like to construct a "corpus" object. This is because I'd like to stem my document set. I built my dtm and vocab