Pakiet R tm używany do analizy predykcyjnej. Jak klasyfikować nowy dokument?

Question

To jest ogólne pytanie dotyczące procedur dotyczących wyszukiwania tekstu. Załóżmy, że mamy Corpus dokumentów sklasyfikowanych jako Spam/No_Spam. W standardowej procedurze można wstępnie przetwarzać dane, usuwać interpunkcje, zatrzymywać słowa itp. Po przekształceniu go w DocumentTermMatrix można zbudować kilka modeli do przewidywania spamu/No_Spam. Oto mój problem. Teraz chcę użyć modelu zbudowanego dla nowych dokumentów. Aby sprawdzić pojedynczy dokument, musiałbym zbudować DocumentTerm * Vector *? więc można go wykorzystać do przewidywania Spamu/No_Spam. W dokumentacji tm znalazłem jeden przekształca cały korpus w macierz, używając na przykład ciężarów tfidf. Jak mogę przekonwertować pojedynczy wektor za pomocą idf z Corpus? czy muszę zmieniać mój korpus i tworzyć za każdym razem nowy DocumentTermMatrix? Przetworzyłem mój korpus, przekształciłem go w macierz, a następnie podzieliłem na zestawy treningowe i testowe. Ale tutaj zestaw testowy został zbudowany w tej samej linii, co matryca dokumentu pełnego zestawu. Mogę sprawdzić dokładność itp., Ale nie wiem, jaka jest najlepsza procedura dla nowej klasyfikacji tekstu.

Ben, wyobraź sobie, że mam wstępnie przetworzony DocumentTextMatrix, konwertuję go na data.frame.

dtm <- DocumentTermMatrix(CorpusProc,control = list(weighting =function(x) weightTfIdf(x, normalize =FALSE),stopwords = TRUE, wordLengths=c(3, Inf), bounds = list(global = c(4,Inf)))) 

dtmDataFrame <- as.data.frame(inspect(dtm)) 

Dodano zmienną czynnikową i zbudowano model.

Corpus.svm<-svm(Risk_Category~.,data=dtmDataFrame) 

Teraz wyobraź sobie, daję ci nowy dokument d (nie było wcześniej w Corpus) i chcesz wiedzieć przewidywania modelu spam/No_Spam. Jak to zrobiłeś?

Ok pozwala stworzyć przykład na podstawie kodu tutaj użytego.

examp1 <- "When discussing performance with colleagues, teaching, sending a bug report or searching for guidance on mailing lists and here on SO, a reproducible example is often asked and always helpful. What are your tips for creating an excellent example? How do you paste data structures from r in a text format? What other information should you include? Are there other tricks in addition to using dput(), dump() or structure()? When should you include library() or require() statements? Which reserved words should one avoid, in addition to c, df, data, etc? How does one make a great r reproducible example?" 
examp2 <- "Sometimes the problem really isn't reproducible with a smaller piece of data, no matter how hard you try, and doesn't happen with synthetic data (although it's useful to show how you produced synthetic data sets that did not reproduce the problem, because it rules out some hypotheses). Posting the data to the web somewhere and providing a URL may be necessary. If the data can't be released to the public at large but could be shared at all, then you may be able to offer to e-mail it to interested parties (although this will cut down the number of people who will bother to work on it). I haven't actually seen this done, because people who can't release their data are sensitive about releasing it any form, but it would seem plausible that in some cases one could still post data if it were sufficiently anonymized/scrambled/corrupted slightly in some way. If you can't do either of these then you probably need to hire a consultant to solve your problem" 
examp3 <- "You are most likely to get good help with your R problem if you provide a reproducible example. A reproducible example allows someone else to recreate your problem by just copying and pasting R code. There are four things you need to include to make your example reproducible: required packages, data, code, and a description of your R environment. Packages should be loaded at the top of the script, so it's easy to see which ones the example needs. The easiest way to include data in an email is to use dput() to generate the R code to recreate it. For example, to recreate the mtcars dataset in R, I'd perform the following steps: Run dput(mtcars) in R Copy the output In my reproducible script, type mtcars <- then paste. Spend a little bit of time ensuring that your code is easy for others to read: make sure you've used spaces and your variable names are concise, but informative, use comments to indicate where your problem lies, do your best to remove everything that is not related to the problem. The shorter your code is, the easier it is to understand. Include the output of sessionInfo() as a comment. This summarises your R environment and makes it easy to check if you're using an out-of-date package. You can check you have actually made a reproducible example by starting up a fresh R session and pasting your script in. Before putting all of your code in an email, consider putting it on http://gist.github.com/. It will give your code nice syntax highlighting, and you don't have to worry about anything getting mangled by the email system." 
examp4 <- "Do your homework before posting: If it is clear that you have done basic background research, you are far more likely to get an informative response. See also Further Resources further down this page. Do help.search(keyword) and apropos(keyword) with different keywords (type this at the R prompt). Do RSiteSearch(keyword) with different keywords (at the R prompt) to search R functions, contributed packages and R-Help postings. See ?RSiteSearch for further options and to restrict searches. Read the online help for relevant functions (type ?functionname, e.g., ?prod, at the R prompt) If something seems to have changed in R, look in the latest NEWS file on CRAN for information about it. Search the R-faq and the R-windows-faq if it might be relevant (http://cran.r-project.org/faqs.html) Read at least the relevant section in An Introduction to R If the function is from a package accompanying a book, e.g., the MASS package, consult the book before posting. The R Wiki has a section on finding functions and documentation" 
examp5 <- "Before asking a technical question by e-mail, or in a newsgroup, or on a website chat board, do the following: Try to find an answer by searching the archives of the forum you plan to post to. Try to find an answer by searching the Web. Try to find an answer by reading the manual. Try to find an answer by reading a FAQ. Try to find an answer by inspection or experimentation. Try to find an answer by asking a skilled friend. If you're a programmer, try to find an answer by reading the source code. When you ask your question, display the fact that you have done these things first; this will help establish that you're not being a lazy sponge and wasting people's time. Better yet, display what you have learned from doing these things. We like answering questions for people who have demonstrated they can learn from the answers. Use tactics like doing a Google search on the text of whatever error message you get (searching Google groups as well as Web pages). This might well take you straight to fix documentation or a mailing list thread answering your question. Even if it doesn't, saying “I googled on the following phrase but didn't get anything that looked promising” is a good thing to do in e-mail or news postings requesting help, if only because it records what searches won't help. It will also help to direct other people with similar problems to your thread by linking the search terms to what will hopefully be your problem and resolution thread. Take your time. Do not expect to be able to solve a complicated problem with a few seconds of Googling. Read and understand the FAQs, sit back, relax and give the problem some thought before approaching experts. Trust us, they will be able to tell from your questions how much reading and thinking you did, and will be more willing to help if you come prepared. Don't instantly fire your whole arsenal of questions just because your first search turned up no answers (or too many). Prepare your question. Think it through. Hasty-sounding questions get hasty answers, or none at all. The more you do to demonstrate that having put thought and effort into solving your problem before seeking help, the more likely you are to actually get help. Beware of asking the wrong question. If you ask one that is based on faulty assumptions, J. Random Hacker is quite likely to reply with a uselessly literal answer while thinking Stupid question..., and hoping the experience of getting what you asked for rather than what you needed will teach you a lesson." 



corpus2 <- Corpus(VectorSource(c(examp1, examp2, examp3, examp4))) 

Uwaga Wyjąłem przykład 5

skipWords <- function(x) removeWords(x, stopwords("english")) 
funcs <- list(tolower, removePunctuation, removeNumbers, stripWhitespace, skipWords) 
corpus2.proc <- tm_map(corpus2, FUN = tm_reduce, tmFuns = funcs) 

corpus2a.dtm <- DocumentTermMatrix(corpus2.proc, control = list(wordLengths = c(3,10))) 
dtmDataFrame <- as.data.frame(inspect(corpus2a.dtm)) 

dodał czynnik zmienny Spam_Classification 2 poziomy spam/No_Spam

dtmFinal<-cbind(dtmDataFrame,Spam_Classification) 

zbudować modelu SVM Corpus.svm < -svm (Spam_Kategoria ~., Data = dtmFinal)

Teraz wyobraź sobie, że h ave przykład 5 jako nowy dokument (e-mail) Jak generuję wartość Spam/No_Spam?

Answer 1

Nie jest jasne, jakie jest Twoje pytanie i jakiego rodzaju odpowiedzi szukasz.

Zakładając, że pytasz "w jaki sposób mogę uzyskać" DocumentTermVector ", aby przekazać inne funkcje?", Oto jedna metoda.

Niektóre powtarzalne dane:

examp1 <- "When discussing performance with colleagues, teaching, sending a bug report or searching for guidance on mailing lists and here on SO, a reproducible example is often asked and always helpful. What are your tips for creating an excellent example? How do you paste data structures from r in a text format? What other information should you include? Are there other tricks in addition to using dput(), dump() or structure()? When should you include library() or require() statements? Which reserved words should one avoid, in addition to c, df, data, etc? How does one make a great r reproducible example?" 
examp2 <- "Sometimes the problem really isn't reproducible with a smaller piece of data, no matter how hard you try, and doesn't happen with synthetic data (although it's useful to show how you produced synthetic data sets that did not reproduce the problem, because it rules out some hypotheses). Posting the data to the web somewhere and providing a URL may be necessary. If the data can't be released to the public at large but could be shared at all, then you may be able to offer to e-mail it to interested parties (although this will cut down the number of people who will bother to work on it). I haven't actually seen this done, because people who can't release their data are sensitive about releasing it any form, but it would seem plausible that in some cases one could still post data if it were sufficiently anonymized/scrambled/corrupted slightly in some way. If you can't do either of these then you probably need to hire a consultant to solve your problem" 
examp3 <- "You are most likely to get good help with your R problem if you provide a reproducible example. A reproducible example allows someone else to recreate your problem by just copying and pasting R code. There are four things you need to include to make your example reproducible: required packages, data, code, and a description of your R environment. Packages should be loaded at the top of the script, so it's easy to see which ones the example needs. The easiest way to include data in an email is to use dput() to generate the R code to recreate it. For example, to recreate the mtcars dataset in R, I'd perform the following steps: Run dput(mtcars) in R Copy the output In my reproducible script, type mtcars <- then paste. Spend a little bit of time ensuring that your code is easy for others to read: make sure you've used spaces and your variable names are concise, but informative, use comments to indicate where your problem lies, do your best to remove everything that is not related to the problem. The shorter your code is, the easier it is to understand. Include the output of sessionInfo() as a comment. This summarises your R environment and makes it easy to check if you're using an out-of-date package. You can check you have actually made a reproducible example by starting up a fresh R session and pasting your script in. Before putting all of your code in an email, consider putting it on http://gist.github.com/. It will give your code nice syntax highlighting, and you don't have to worry about anything getting mangled by the email system." 
examp4 <- "Do your homework before posting: If it is clear that you have done basic background research, you are far more likely to get an informative response. See also Further Resources further down this page. Do help.search(keyword) and apropos(keyword) with different keywords (type this at the R prompt). Do RSiteSearch(keyword) with different keywords (at the R prompt) to search R functions, contributed packages and R-Help postings. See ?RSiteSearch for further options and to restrict searches. Read the online help for relevant functions (type ?functionname, e.g., ?prod, at the R prompt) If something seems to have changed in R, look in the latest NEWS file on CRAN for information about it. Search the R-faq and the R-windows-faq if it might be relevant (http://cran.r-project.org/faqs.html) Read at least the relevant section in An Introduction to R If the function is from a package accompanying a book, e.g., the MASS package, consult the book before posting. The R Wiki has a section on finding functions and documentation" 
examp5 <- "Before asking a technical question by e-mail, or in a newsgroup, or on a website chat board, do the following: Try to find an answer by searching the archives of the forum you plan to post to. Try to find an answer by searching the Web. Try to find an answer by reading the manual. Try to find an answer by reading a FAQ. Try to find an answer by inspection or experimentation. Try to find an answer by asking a skilled friend. If you're a programmer, try to find an answer by reading the source code. When you ask your question, display the fact that you have done these things first; this will help establish that you're not being a lazy sponge and wasting people's time. Better yet, display what you have learned from doing these things. We like answering questions for people who have demonstrated they can learn from the answers. Use tactics like doing a Google search on the text of whatever error message you get (searching Google groups as well as Web pages). This might well take you straight to fix documentation or a mailing list thread answering your question. Even if it doesn't, saying “I googled on the following phrase but didn't get anything that looked promising” is a good thing to do in e-mail or news postings requesting help, if only because it records what searches won't help. It will also help to direct other people with similar problems to your thread by linking the search terms to what will hopefully be your problem and resolution thread. Take your time. Do not expect to be able to solve a complicated problem with a few seconds of Googling. Read and understand the FAQs, sit back, relax and give the problem some thought before approaching experts. Trust us, they will be able to tell from your questions how much reading and thinking you did, and will be more willing to help if you come prepared. Don't instantly fire your whole arsenal of questions just because your first search turned up no answers (or too many). Prepare your question. Think it through. Hasty-sounding questions get hasty answers, or none at all. The more you do to demonstrate that having put thought and effort into solving your problem before seeking help, the more likely you are to actually get help. Beware of asking the wrong question. If you ask one that is based on faulty assumptions, J. Random Hacker is quite likely to reply with a uselessly literal answer while thinking Stupid question..., and hoping the experience of getting what you asked for rather than what you needed will teach you a lesson." 

utworzyć korpus z tych tekstów:

corpus2 <- Corpus(VectorSource(c(examp1, examp2, examp3, examp4, examp5))) 

tekst Proces:

skipWords <- function(x) removeWords(x, stopwords("english")) 
funcs <- list(tolower, removePunctuation, removeNumbers, stripWhitespace, skipWords) 
corpus2.proc <- tm_map(corpus2, FUN = tm_reduce, tmFuns = funcs) 

Konwersja przetwarzane korpusy do Term matrycy dokumentu:

corpus2a.dtm <- DocumentTermMatrix(corpus2.proc, control = list(wordLengths = c(3,10))) 
inspect(corpus2a.dtm) 

A document-term matrix (5 documents, 273 terms) 

Non-/sparse entries: 314/1051 
Sparsity   : 77% 
Maximal term length: 10 
Weighting   : term frequency (tf) 

    Terms 
Docs able actually addition allows answer answering answers archives are arsenal avoid background based 
    1 0  0  2  0  0   0  0  0 1  0  1   0  0 
    2 1  1  0  0  0   0  0  0 0  0  0   0  0 
    3 0  1  0  1  0   0  0  0 0  0  0   0  0 
    4 0  0  0  0  0   0  0  0 0  0  0   1  0 
    5 2  1  0  0  8   2  3  1 0  1  0   0  1 

To jest klucz linia, która dostaje się „DocumentTerm * Vector *”, które odnoszą się do:

# access vector of first document in the dtm 
as.matrix(corpus2a.dtm)[1,] 

able actually addition  allows  answer answering answers archives  are 
     0   0   2   0   0   0   0   0   1 
    arsenal  avoid background  based  basic  before  better  beware  bit 
     0   1   0   0   0   0   0   0   0 
    board  book  bother  bug changed  chat  check  

W rzeczywistości jest to nazwane numer, który powinien być przydatny do przekazywania do innych funkcji , itd., które wydaje się, że to, co chcesz zrobić:

str(as.matrix(corpus2a.dtm)[1,]) 
Named num [1:273] 0 0 2 0 0 0 0 0 1 0 ... 

Jeśli chcesz tylko wektor numeryczną, spróbuj as.numeric(as.matrix(corpus2a.dtm)[1,]))

Czy to właśnie chcesz robić?

Answer 2

Mam ten sam problem i myślę, że pakiet RTextTools może ci pomóc.

Spójrz na create_matrix:

... 
originalMatrix - The original DocumentTermMatrix used to train the models. If supplied, will 
adjust the new matrix to work with saved models. 
...

Więc w kodzie:

train.data <- loadDataTable() # load data from DB - 3 columns (info, subject, category) 
train.matrix <- create_matrix(train.data[, c(subject, info)]), language="english", removeNumbers=TRUE, stemWords=FALSE, weighting=weightTfIdf) 
train.container <- create_container(train.matrix,train.data$category,trainSize=1:nrow(train.data), virgin=FALSE) 
model <- train_model(train.container, algorithm=c("SVM")) 
# save model & matrix 

predict.text <- function(info, subject, train.matrix, model) 
{ 
    predict.matrix <- create_matrix(cbind(subject = subject, info = info), originalMatrix = train.matrix, language="english", removeNumbers=TRUE, stemWords=FALSE, weighting=weightTfIdf) 
    predict.container <- create_container(predict.matrix, NULL, testSize = 1, virgin = FALSE) # testSize = 1 - we have only one row! 
    return(classify_model(predict.container, model)) 
} 

Answer 3

Dzięki za to ciekawe pytanie. Myślałem o tym przez jakiś czas. Zbyt krótka sprawa, kwintesencja moich ustaleń: dla metod ważenia oprócz tf nie ma mowy o pracochłonnej pracy ani ponownym przeliczaniu całego DTM (i prawdopodobnie ponownym uruchomieniu svm).

Tylko dla wagi tf mogłem znaleźć łatwy proces klasyfikacji nowej treści. Musisz przekształcić nowy dokument (na pewno) w DTM. Podczas transformacji musisz dodać dictionary zawierający wszystkie terminy używane do trenowania klasyfikatora na starym korpusie. Wtedy możesz normalnie używać predict(). Dla części TF tutaj bardzo minimalnym próbki i sposobu klasyfikacji nowy dokument:

### I) Data 

texts <- c("foo bar spam", 
      "bar baz ham", 
      "baz qux spam", 
      "qux quux ham") 

categories <- c("Spam", "Ham", "Spam", "Ham") 

new <- "quux quuux ham" 

### II) Building Model on Existing Documents „texts“ 

library(tm) # text mining package for R 
library(e1071) # package with various machine-learning libraries 

## creating DTM for texts 
dtm <- DocumentTermMatrix(Corpus(VectorSource(texts))) 

## making DTM a data.frame and adding variable categories 
df <- data.frame(categories, as.data.frame(inspect(dtm))) 

model <- svm(categories~., data=df) 

### III) Predicting class of new 

## creating dtm for new 
dtm_n <- DocumentTermMatrix(Corpus(VectorSource(new)), 
          ## without this line predict won't work 
          control=list(dictionary=names(df))) 
## creating data.frame for new 
df_n <- as.data.frame(inspect(dtm_n)) 

predict(model, df_n) 

## > 1 
## > Ham 
## > Levels: Ham Spam