tm

actually I am trying to do a sentiment analysis based on twitter data using the naive bayes algorithm. I have a look on 2000 Tweets. After getting the data into R studio I split and preprocess the date as follows: train_size = floor(0.75 * nrow(Tweets_Model_Input)) set.seed(123) train_sub = sample(seq_len(nrow(Tweets_Model_Input)), size = train_size) Tweets_Model_Input_Train = Tweets_Model_Input[train_sub, ] Tweets_Model_Input_Test = Tweets_Model_Input[-train_sub, ] myCorpus = Corpus(VectorSource(Tweets_Model_Input_Train$SentimentText)) myCorpus <- tm_map(myCorpus, removeWords, stopwords(

how can I convert a corpus into a data frame in R which contains also meta data? I already tried the suggestion from convert corpus into data.frame in R , but the resulting data frame only contains the text lines from all docs in the corpus. I need also the document ID and maybe the line number of the text line in two columns. So, how can I extend this command: dataframe <- data.frame(text=unlist(sapply(mycorpus, [ , "content")), stringsAsFactors=FALSE) to get the data? I already tried dataframe <- data.frame(id=sapply(corpus, meta(corpus, "id")), text=unlist(sapply(corpus, `[`, "content")),

I have 4000 text documents in corpus. I want to remove row(s) that contains a specific word from each document as a part of data clean up. For example: library(tm) doc.corpus<- VCorpus(DirSource("C:\\TextMining\\Prototype",pattern="*.txt",encoding= "UTF8",mode = "text"),readerControl=list(language="en")) doc.corpus<- tm_map(doc.corpus, PlainTextDocument) doc.corpus[[1]] #PlainTextDocument Metadata: 7 Content: chars: 16542 as.character(doc.corpus)[[1]] $content "Quick to deploy, easy to use, and offering complete investment protection, our product is clearly differentiated from all competitive

I am new using the tm package. I want to read a csv file which contents one column with 2000 texts and a second column with a factor variable yes/no into a Corpus. My intention is to convert the text as a matrix and use the factor variable as target for prediction. I would need to divide the corpus in train and test sets as well. I read several documents like tm.pdf etc. and found the documentation relatively limited. This is my attempt following another threat on the same subject, TexTest<-read.csv("C:/Test.csv") m <- list(Text = "Text", Clasification = "Classification") corpus1 <- Corpus(x

I have a corpus that I'm using the tm package on in R (and also mirroring the same script in NLTK in python). I'm working with unigrams, but would like a parser of some kind to combine words commonly co-located to be as if one word---ie, I'd like to stop seeing "New" and "York" separately in my data set when they occur together, and see this particular pair represented as "New York" as if that were a single word, and alongside other unigrams. What is this process called, of transforming meaningful, common n-grams onto the same footing as unigrams? Is it not a thing? Finally, what would the tm