text-mining

I have been trying to replicate the example here : but I have had some problems along the way. Everything worked fine until here: docsTDM <- TermDocumentMatrix(docs8) Error in UseMethod("meta", x) : no applicable method for 'meta' applied to an object of class "character" In addition: Warning message: In mclapply(unname(content(x)), termFreq, control) : all scheduled cores encountered errors in user code So I was able to fix that error modifying this previous step by changing this: docs8 <- tm_map(docs7, tolower) To this: docs8 <- tm_map(docs7, content_transformer(tolower)) But then I got in

I was hoping you'd be able to assist in a text mining exercise. I was interested in 'AAPL' tweets, and was able to pull 500 tweets from the API. I was able to clear several hurdles on my own, but need help for last part. For some reason, the tm package is not removing stopwords. Can you please take a look and see what the problem might be? Are emoticons causing an issue? After plotting Term_Frequency, the most frequent terms are "AAPL", "Apple", "iPhone", "Price", "Stock" Thanks in advance! Munckinn transform into dataframe tweets.df <- twListToDF(tweets) #Isolate text from tweets aapl_tweets

I'm creating a wordcloud in which the size of the words is based on frequency, but i want the colour of the words to be mapped to a third variable (stress, which is the amount of stress associated with each word, a numerical or continuous variable). I tried the following, which gave me only two different colours (yellow and purple) while i want something more smooth. I would like some color range like a palette that goes from green to red for example. df = data.frame(word = c("calling", "meeting", "conference", "contract", "negotiation", "email"), n = c(20, 12, 4, 8, 10, 43), stress = c(23, 30

Can someone share a code snippet that shows how to use SVM for text mining using scikit. I have seen an example of SVM on numerical data but not quite sure how to deal with text. I looked at http://scikit-learn.org/stable/auto_examples/document_classification_20newsgroups.html but couldn't find SVM. In text mining problems, text is represented by numeric values. Each feature represent a word and values are binary numbers. That gives a matrix with lots of zeros and a few 1s which means that the corresponding words exist in the text. Words can be given some weights according to their frequency

i am using rapidminer 5.3.I took a small document which contains around three english sentences , tokenized it and filtered it with respect to the length of words.i want to write the output into a different word document.i tried using Write document utility but it is not working,it is simply writing the same original document into the new one.However when i write the output to the console,it gives me the expected answer.Something wrong with the write document utility. Here is my process READ DOCUMENT --> TOKENIZE --> FILTER TOKENS --> WRITE DOCUMENT Try the following Cut Document (with (\S+)