我在R中使用TermDocumentMatrix包创建了多个tm。
我想在每组文档中找到10个最常用的术语，最终得到如下输出表：
corpus1 corpus2
"beach" "city"
"sand" "sidewalk"
... ...
[10th most frequent word]
根据定义，findFreqTerms(corpus1,N)会返回出现N次或更多次的所有字词。要手动执行此操作，我可以更改N，直到我返回10个左右的术语，但findFreqTerms的输出按字母顺序列出，所以除非我选择了正确的N，否则我实际上不知道哪个是前10个我怀疑这涉及操纵你可以用str(corpus1) {{1}}看到的TDM的内部结构，但这里的答案对我来说是非常不透明的，所以我想重新解释这个问题。
谢谢！
答案 0 :(得分：30)
这是在文档术语矩阵中查找前N个术语的一种方法。简而言之，您将dtm转换为矩阵，然后按行总和进行排序：
# load text mining library
library(tm)
# make corpus for text mining (data comes from package, for reproducibility)
data("crude")
corpus <- Corpus(VectorSource(crude))
# process text (your methods may differ)
skipWords <- function(x) removeWords(x, stopwords("english"))
funcs <- list(tolower, removePunctuation, removeNumbers, stripWhitespace, skipWords)
a <- tm_map(corpus, FUN = tm_reduce, tmFuns = funcs)
a.dtm1 <- TermDocumentMatrix(a, control = list(wordLengths = c(3,10)))
这是Q中的方法，它按字母顺序返回单词，并不总是非常有用，正如您所注意到的......
N <- 10
findFreqTerms(a.dtm1, N)
[1] "barrel" "barrels" "bpd" "crude" "dlrs" "government" "industry" "kuwait"
[9] "market" "meeting" "minister" "mln" "month" "official" "oil" "opec"
[17] "pct" "price" "prices" "production" "reuter" "saudi" "sheikh" "the"
[25] "world"
这就是你可以做的事情，以便按照丰富的顺序获得前N个单词：
m <- as.matrix(a.dtm1)
v <- sort(rowSums(m), decreasing=TRUE)
head(v, N)
oil prices opec mln the bpd dlrs crude market reuter
86 48 47 31 26 23 23 21 21 20
对于多个文档术语矩阵，您可以执行以下操作：
# make a list of the dtms
dtm_list <- list(a.dtm1, b.dtm1, c.dtm1, d.dtm1)
# apply the rowsums function to each item of the list
lapply(dtm_list, function(x) sort(rowSums(as.matrix(x)), decreasing=TRUE))
这就是你想要做的吗？
我第一次看到这种方法的Ian Fellows'wordcloud包的小费。
更新：按照下面的评论，这里有一些更详细的信息......
以下是使用多个语料库制作可重现示例的一些数据：
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."
现在让我们以通常的方式处理示例文本。首先将字符向量转换为语料库。
library(tm)
list_examps <- lapply(1:5, function(i) eval(parse(text=paste0("examp",i))))
list_corpora <- lapply(1:length(list_examps), function(i) Corpus(VectorSource(list_examps[[i]])))
现在删除停用词，数字，标点符号等
skipWords <- function(x) removeWords(x, stopwords("english"))
funcs <- list(tolower, removePunctuation, removeNumbers, stripWhitespace, skipWords)
list_corpora1 <- lapply(1:length(list_corpora), function(i) tm_map(list_corpora[[i]], FUN = tm_reduce, tmFuns = funcs))
将已处理的语料库转换为术语文档矩阵：
list_dtms <- lapply(1:length(list_corpora1), function(i) TermDocumentMatrix(list_corpora1[[i]], control = list(wordLengths = c(3,10))))
获取每个语料库中最常出现的单词：
top_words <- lapply(1:length(list_dtms), function(x) sort(rowSums(as.matrix(list_dtms[[x]])), decreasing=TRUE))
根据指定的格式将其重塑为数据框：
library(plyr)
top_words_df <- t(ldply(1:length(top_words), function(i) head(names(top_words[[i]]),10)))
colnames(top_words_df) <- lapply(1:length(list_dtms), function(i) paste0("corpus",i))
top_words_df
corpus1 corpus2 corpus3 corpus4 corpus5
V1 "example" "data" "code" "functions" "answer"
V2 "addition" "people" "example" "prompt" "help"
V3 "data" "synthetic" "easy" "relevant" "try"
V4 "how" "able" "email" "book" "question"
V5 "include" "actually" "include" "keywords" "questions"
V6 "what" "bother" "recreate" "package" "reading"
V7 "when" "consultant" "script" "posting" "answers"
V8 "are" "cut" "check" "read" "people"
V9 "avoid" "form" "data" "search" "search"
V10 "bug" "happen" "mtcars" "section" "searching"
您可以根据自己的数据进行调整吗？如果没有，请编辑您的问题，以便更准确地显示您的数据。