text-analysis

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 am trying to build a matrix where the first row will be a part of speech, first column a sentence. values in the matrix should show the number of such POS in a sentence. So I am creating POS tags in this way: data = pd.read_csv(open('myfile.csv'),sep=';') target = data["label"] del data["label"] data.sentence = data.sentence.str.lower() # All strings in data frame to lowercase for line in data.sentence: Line_new= nltk.pos_tag(nltk.word_tokenize(line)) print(Line_new) The output is: [('together', 'RB'), ('with', 'IN'), ('the', 'DT'), ('6th', 'CD'), ('battalion', 'NN'), ('of', 'IN'), ('the',

this is the text in my dataframe df which has a text column called 'problem_note_text' SSCIssue: Note Dispenser Failureperformed checks / dispensor failure / asked the stores to take the note dispensor out and set it back / still error message says front door is open / hence CE attn reqContact details - Olivia taber 01159063390 / 7am-11pm df$problem_note_text <- tolower(df$problem_note_text) df$problem_note_text <- tm::removeNumbers(df$problem_note_text) df$problem_note_text<- str_replace_all(df$problem_note_text, " ", "") # replace double spaces with single space df$problem_note_text = str

I have a data set (Facebook posts) (via netvizz) and I use the quanteda package in R. Here is my R code. # Load the relevant dictionary (relevant for analysis) liwcdict <- dictionary(file = "D:/LIWC2001_English.dic", format = "LIWC") # Read File # Facebooks posts could be generated by FB Netvizz # https://apps.facebook.com/netvizz # Load FB posts as .csv-file from .zip-file fbpost <- read.csv("D:/FB-com.csv", sep=";") # Define the relevant column(s) fb_test <-as.character(FB_com$comment_message) #one column with 2700 entries # Define as corpus fb_corp <-corpus(fb_test) class(fb_corp) # LIWC

I am analyzing the Reuters 21578 corpus, all the Reuters news articles from 1987, using the "tm" package. After importing the XML files into an R data file, I clean the text--convert to plaintext, convert to lwer case, remove stop words etc. (as seen below)--then I try to convert the corpus to a document term matrix but receive an error message: Error in UseMethod("Content", x) : no applicable method for 'Content' applied to an object of class "character" All pre-processing steps work correctly up until document term matrix. I created a non-random subset of the corpus (with 4000 documents) and

I want to analyse a field of 100 character length and estimate similarity %. For example, for a same question "Whats your opinion on smartphone?", Person A: "Best way to waste money" Person B: "Amazing stuff. lets you stay connected all the time" Person C: "Instrument to waste money and time" Out of these, just by matching individual words, A and C sound similar. I am trying to do something like this to start with in R and later on extend to match combination of words like "Best", "Best way", "Best way waste" etc. I am newbie to text analysis and R and could not get the proper naming of these

I have been breaking my head over this one over the last few days. I searched all the SO archives and tried the suggested solutions but just can't seem to get this to work. I have sets of txt documents in folders such as 2000 06, 1995 -99 etc, and want to run some basic text mining operations such as creating document term matrix and term document matrix and doing some operations based co-locations of words. My script works on a smaller corpus, however, when I try it with the bigger corpus, it fails me. I have pasted in the code for one such folder operation. library(tm) # Framework for text