text-mining

问题 I am trying to extract the identified dictionary words from a Quanteda dfm, but have been unable to find a solution. Does someone have a solution for this? Sample input: dict <- dictionary(list(season = c("spring", "summer", "fall", "winter"))) dfm <- dfm("summer is great", dictionary = dict) Output: > dfm Document-feature matrix of: 1 document, 1 feature. 1 x 1 sparse Matrix of class "dfmSparse" features docs season text1 1 I now know that a seasonality dict word has been identified in the

问题 loading a bunch of documents using tm Corpus i need to specify encoding. All documents are UTF-8 encoded. If openend via text editor content is ok but corpus contents is full of strange symbols (indicioâ., ‘sœs....) Source text is in spanish. ES_es library(tm) cname <- file.path("C:", "Users", "john", "Documents", "texts") docs <- Corpus(DirSource(cname), encoding ="UTF-8") > Error in Corpus(DirSource(cname), encoding = "UTF-8") : unused argument (encoding = "UTF-8") EDITED: Getting str

问题 I want to use grepl for multiple patterns defined as a data frame. df_sen is presented as sentence "She would like to go there" "I had it few days ago" "We have spent few millions" df_triggers is presented as follows: trigger few days few millions And I want to create a matrix where sentence x triggers and on the intersection to see 1 if trigger was found in a sentence and 0 if it was not. I have tried to do it like this: matrix <- grepl(df_triggers$trigger, df_sen$sentence) But I see the

问题 # init libs <- c("tm", "plyr", "class", "RTextTools", "randomForest") lapply(libs, require, character.only = TRUE) # set options options(stringsAsFactors = FALSE) # set parameters labels <- read.table('labels.txt') path <- paste(getwd(), "/data", sep="") # clean text cleanCorpus <- function(corpus) { corpus.tmp <- tm_map(corpus, removePunctuation) corpus.tmp <- tm_map(corpus.tmp, removeNumbers) corpus.tmp <- tm_map(corpus.tmp, stripWhitespace) corpus.tmp <- tm_map(corpus.tmp, content

问题 I have a following line x<-"CUST_Id_8Name:Mr.Praveen KumarDOB:Mother's Name:Contact Num:Email address:Owns Car:Products held with Bank:Company Name:Salary per. month:Background:" I want to extract "CUST_Id_8", "Mr. Praveen Kumar" and anything written after DOB: Mother's name: Contact Num: and so on stored in variables like Customer Id, Name, DOB and so on. Please help. I used strsplit(x, ":") But the result is a list containing the texts. But I need blanks if there is nothing after the

问题 Say that I have a table that contains probabilities from each words from another table. This Table has 2 classes; actual and non_actual . I will name it master_table actual = [0.5;0.4;0.6;0.75;0.23;0.96;0.532]; %sum of the probabilities is 1. actual + non_actual = 1 non_actual = [0.5;0.6:0.4;0.25;0.77;0.04;0.468]; words = {'finn';'jake';'iceking';'marceline';'shelby';'bmo';'naptr'}; master_table = table(actual,non_actual,... 'RowNames',words) And then I have a table that contains sentences. I

问题 I am tring to read my tweets from a csv file (which I have downloaded previously), and I am having some problems: sia.list <- searchTwitter('#singaporeair', n=10, since=NULL, until=NULL, cainfo="cacert.pem") sia.df = twListToDF(sia.list) write.csv(sia.df, file='C:/temp/siaTweets.csv', row.names=F) I am trying to extract the text from the list and the problems is with the third line below: sia.df <- read.csv(file=paste(path,"siaTweets.csv",sep="")) sia.list <- as.list(t(sia.df)) sia_txt =

问题 In my dataset I am trying to create variables containing the number of nouns, verbs and adjectives, respectively for each observation. Using the openNLP package I have managed to get this far: s <- paste(c("Pierre Vinken, 61 years old, will join the board as a ", "nonexecutive director Nov. 29.\n", "Mr. Vinken is chairman of Elsevier N.V., ", "the Dutch publishing group."), collapse = "") s <- as.String(s) s sent_token_annotator <- Maxent_Sent_Token_Annotator() word_token_annotator <- Maxent

问题 I'm supposed to take two sentences each time and compute if they are similar. By similar I mean, both syntactically and semantically. INPUT1: Obama signs the law. A new law is signed by Obama. INPUT2: A Bus is stopped here. A vehicle stops here. INPUT3: Fire in NY. NY is burnt down. INPUT4: Fire in NY. 50 died in NY fire. I don't want to use ontology tree as a soul. I wrote a code to compute Levenshtein distance (LD) between sentences and then decide if the 2nd sentence: can be ignored

问题 How do you find the associated words to the eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) (NRC Word-Emotion Association Lexicon) when using get_nrc_sentiment of the using the syuzhet package? a <- c("I hate going to work it is dull","I love going to work it is fun") a_corpus = Corpus(VectorSource(a)) a_tm <- TermDocumentMatrix(a_corpus) a_tmx <- as.matrix(a_tm) a_df<-data.frame(text=unlist(sapply(a, `[`)), stringsAsFactors=F) a_sent<-get_nrc