corpus

I am trying to work with the tm package in R, and have a CSV file of customer feedback with each line being a different instance of feedback. I want to import all the content of this feedback into a corpus but I want each line to be a different document within the corpus, so that I can compare the feedback in a DocTerms Matrix. There are over 10,000 rows in my data set. Originally I did the following: fdbk_corpus <-Corpus(VectorSource(fdbk), readerControl = list(language="eng"), sep="\t") This creates a corpus with 1 document and >10,000 rows, and I want >10,000 docs with 1 row each. I imagine

I have several TermDocumentMatrix s created with the tm package in R. I want to find the 10 most frequent terms in each set of documents to ultimately end up with an output table like: corpus1 corpus2 "beach" "city" "sand" "sidewalk" ... ... [10th most frequent word] By definition, findFreqTerms(corpus1,N) returns all of the terms which appear N times or more. To do this by hand I could change N until I got 10 or so terms returned, but the output for findFreqTerms is listed alphabetically so unless I picked exactly the right N, I wouldn't actually know which were the top 10. I suspect that

My file has over 4M rows and I need a more efficient way of converting my data to a corpus and document term matrix such that I can pass it to a bayesian classifier. Consider the following code: library(tm) GetCorpus <-function(textVector) { doc.corpus <- Corpus(VectorSource(textVector)) doc.corpus <- tm_map(doc.corpus, tolower) doc.corpus <- tm_map(doc.corpus, removeNumbers) doc.corpus <- tm_map(doc.corpus, removePunctuation) doc.corpus <- tm_map(doc.corpus, removeWords, stopwords("english")) doc.corpus <- tm_map(doc.corpus, stemDocument, "english") doc.corpus <- tm_map(doc.corpus,

My project uses the NLTK. How can I list the project's corpus & model requirements so they can be automatically installed? I don't want to click through the nltk.download() GUI, installing packages one by one. Also, any way to freeze that same list of requirements (like pip freeze )? The NLTK site does list a command line interface for downloading packages and collections at the bottom of this page : http://www.nltk.org/data The command line usage varies by which version of Python you are using, but on my Python2.6 install I noticed I was missing the 'spanish_grammar' model and this worked

The NLTK word corpus does not have the phrase "okay", "ok", "Okay"? > from nltk.corpus import words > words.words().__contains__("check") > True > words.words().__contains__("okay") > False > len(words.words()) > 236736 Any ideas why? alvas TL;DR from nltk.corpus import words from nltk.corpus import wordnet manywords = words.words() + wordnet.words() In Long From the docs , the nltk.corpus.words are words a list of words from " http://en.wikipedia.org/wiki/Words_(Unix) Which in Unix, you can do: ls /usr/share/dict/ And reading the README: $ cd /usr/share/dict/ /usr/share/dict$ cat README # @(#

Please see the tutorial steps for OpenNLP - Named Entity Recognition : Link to tutorial I am using the "en-ner-person.bin" model found here In the tutorial, there are instructions on Training and creating a new model. Is there any way to "Update" the existing "en-ner-person.bin" with additional training data? Say I have a list of 500 additional person names that are otherwise not recognized as persons - how do I generate a new model? Sorry it took me a while to put together a decent code example... What the code below does is read in your sentences, uses the default en-ner-person model to do

From Creating a subset of words from a corpus in R , the answerer can easily convert a term-document matrix into a word cloud easily. Is there a similar function from python libraries that takes either a raw word textfile or NLTK corpus or Gensim Mmcorpus into a word cloud? The result will look somewhat like this: Here's a blog post which does just that: http://peekaboo-vision.blogspot.com/2012/11/a-wordcloud-in-python.html The whole code is here: https://github.com/amueller/word_cloud HeadAndTail from wordcloud import WordCloud, STOPWORDS import matplotlib.pyplot as plt stopwords = set