text-mining

How do you print a small sample, or first line, of a corpus in R using the tm package? I have a very large corpus ( > 1 GB) and am doing some text cleaning. I would like to test as I apply cleaning procedures. Printing just the first line, or first few lines of a corpus would be ideal. # Load Libraries library(tm) # Read in Corpus corp <- SimpleCorpus( DirSource( "C:/TextDocument")) # Remove puncuation corp <- removePunctuation(corp, preserve_intra_word_contractions = TRUE, preserve_intra_word_dashes = TRUE) I have tried accessing the corpus several ways: # Print first line of first element of

Hi I have a list of words which have been stemmed using the "tm" package in R. Can I get back the root word some how after this step. Thanks in Advance. Ex : activiti --> activity You can use the stemCompletion() function to achieve this, but you may need to trim the stems first. Consider the following: library(tm) library(qdap) # providers the stemmer() function active.text = "there are plenty of funny activities" active.corp = Corpus(VectorSource(active.text)) (st.text = tolower(stemmer(active.text,warn=F))) # this is what the columns of your Term Document Matrix are going to look like [1]

I am using the 'tm' package in R to create a term document matrix using stemmed terms. The process is completing, but the resulting matrix includes terms that don't appear to have been stemmed, and I'm trying to understand why that is and how to fix it. Here is the script for the process, which uses a couple of online news stories as the sandbox: library(boilerpipeR) library(RCurl) library(tm) # Pull the relevant parts of the news stories using 'boilerpipeR' and 'RCurl' url <- "http://blogs.wsj.com/digits/2015/07/14/google-mozilla-disable-flash-over-security-concerns/" extract <-

I want to know what can be used to determine the relevance of a page for a theme like games, movies, etc. Is there some research in this area or is there only counting how many times some relevant words appear? The common choice is supervised document classification on bag of words (or bag of n-grams) features, preferably with tf-idf weighting. Popular algorithms include Naive Bayes and (linear) SVMs. For this approach, you'll need labeled training data, i.e. documents annotated with relevant themes. See, e.g., Introduction to Information Retrieval , chapters 13-15. 来源： https://stackoverflow

I have corpus roughly with 75 MB data. I am trying to use the following command tm_map(doc.corpus, removeWords, stopwords("english")) tm_map(doc.corpus, tolower) This two alone functions are taking at least 40 mins to run. I am looking for speeding up the process as I am using tdm matrix for my model. I have tried commands like gc() and memory.limit(10000000) very frequently but I am not able to speed up my process speed. I have a system with 4GB RAM and running a local database to read the input data. Hoping for suggestions to speed up! Maybe you can give quanteda a try library(stringi)

How to classify strings using r My text file is such a structure. >cell_c2< 8/30/2017 This location has been closed for a few months. Recently I passed by and attracted by their street sign Teriyaki Grill Open. I gave a try. The cashier was friendly and recommended me to try their most popular Teriyaki chicken box. It came with mixed vege and steamed rice. They have an open kitchen with SS equipment. I could see the chef make grill after my order was placed. I love the teriyaki chicken with white rice. The full box costs $8 after tax. I think it's pretty reasonable for what you get near the