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############################################################### # Matthew L. Jockers # mjockers@unl.edu # Text Analysis and Topic Modeling in the Humanities Workshop # University of Wisconsin-Milwaukee # April 19, 2013 ############################################################### ############################################################### # SESSION ONE (9:00-10:15) ############################################################### ############################################# # 1.1 The R computing environment--what is R ############################################# ############################################# # 1.2 R console vs. RStudio show both quickly ############################################# ############################################# # 1.3 Vectors and basic math in R ############################################# # Explain the assignment operator "<-" myvar<-1 myvar length(myvar) # Notice that myvar is displayed with a [1]. R uses vectors to store most everything. a vector is simply a list of items. in this case myvar is a vector of one item # now we create a new vector with 4 itmes using the "c" function myvar<-c(1,2,4,6) # the "c" function means "combine" myvar length(myvar) class(myvar) # show numeric class #vectors can contain different types of data. myvar<-c('a', 'b', 'c') class(myvar) # show character class # But Vectors cannot hold values with different data types in the same vector myvar<-c("a", "b", 1, 2, 3) myvar # Notice that R has converted the numbers to characters. # R has lots of handy shortcuts myvar<-5:25 myvar # Accessing items in a vector can get confusing. think about each item as having a numerical index from 1 to n # in the sequence we just created, the first item in the vector list is the number 5. We can access items in a vector by putting their index number inside square brackets, like this myvar[1] # and if we want to see the first three items myvar[1:3] # ? huh ? Yes, this can be a bit confusing, especially when we use numbers as items in the vector. # consider this instead myvar<-c("This", "is", "a", "test", ".") myvar[1:3] myvar[3:5] # Cool. Now back to numbers for a minute. With items in vectors, we can do vector math. . . myvar<-c(1,2,4,6) myvar+10 #notice that R "recycles" the 10 over each value in the vector. this is very handy and very important. # it can get even more fun when you have two vectors, like this myvar1<-c(1,2,4,6) myvar2<-c(10, 20,40,60) # you can add them "index" wise across vectors. myvar1+myvar2 # you can also use R as a calculator. . . 1+1 2-3 3*3 8675309/3 # that's enough for now . . . let's play with some text ############################################# # 1.4 Text manipulation in R ############################################# # Load Plain text of Moby Dick from Project Gutenberg using the scan function text<-scan("http://www.gutenberg.org/cache/epub/2701/pg2701.txt", what="character", sep="\n") class(text) # text is a character vector # what is that \n thingy? length(text) #[1] 18874 text[1:3] #explain line based chunking when using \n # Explore how the text is ingested as lines. text[1:407] #Introductory material and boilerplate text[408] #start of first chapter text[18577] #beginning of the final boilerplate class(text) str(text) # Demonstrate the use of the which function as a way to find items in vector start<-which(text == "CHAPTER 1. Loomings.") start end<-which(text == "End of Project Gutenberg's Moby Dick; or The Whale, by Herman Melville") end start<-408 end<-18577 # instead of this: novel.lines<- text[408:(18577-1)] # we can use the start and end variables # Now we can easily isolate the main text of the novel novel.lines<- text[start:(end-1)] # why the -1 length(novel.lines) novel.lines[1] # so now we've got 18169 lines. # we want a single string of text # so we can use paste to glue all of the lines together novel<-paste(novel.lines, collapse=" ") length(novel) # huh? What happened here. . . . # now we want to begin doing some analysis, let's convert all to lowercase novel.lower <-tolower(novel) class(novel.lower) novel.lower # check it out y'all moby.words<-strsplit(novel.lower, "\\W") # huh? wassup with that \\W, man? class(moby.words) # a list, what the heck is a list? length(moby.words) # well, it is a list of one, and the one happens to be a vector # we can access the item(s) in a list using double brackets # Here I'll show the entire vector of words that is in item 1 moby.words[[1]] # and we can see how many items are in the vector in the first list item, like this length(moby.words[[1]]) # Well, in this case the list seems extraneous. In fact it is. # We only have a list here because that is the way that strsplit outputs # its results. So let's get rid of it. moby.word.vector<-unlist(moby.words) length(moby.word.vector) # hey, that's the same as this: length(moby.words[[1]]) # if you look at the vector of words you'll see some odd "blanks" moby.word.vector[1:20] # ? huh. . . when we use \\W, punctuation gets stripped and these remindered of where the punc chars were get left as a residue. # we can figure out the POSITIONS of these blanks using which which(moby.word.vector=="") # and we can figure out which are not blanks by using the not equals operator != not.blanks<-which(moby.word.vector!="") # so, not.blanks is now a vector of the "positions" of items in the moby.word.vector that are not blanks. # so we can overwrite the existing moby.word.vector with a pared down version that omits the blanks, like this moby.word.vector<- moby.word.vector[not.blanks] # once we have all the words in a vector, we can do some cool searching: whales<-which(moby.word.vector=="whale") whale.hits<-length(whales) whale.hits/length(moby.word.vector) # relative frequency of whales # compare to "the" the<-which(moby.word.vector=="the") the.hits<-length(the) the.hits/length(moby.word.vector) # we can make the percentages easier to read 100*(the.hits/length(moby.word.vector)) 100*(whale.hits/length(moby.word.vector)) # we can figure out how many unique words there are in the novel length(unique(moby.word.vector)) # that's big! # we can even create an entire frequency list for all the words moby.freqs<-table(moby.word.vector) sorted.moby.freqs<-sort(moby.freqs , decreasing=TRUE) sorted.moby.freqs[1:10] sorted.moby.rel.freqs<-100*(sorted.moby.freqs/sum(sorted.moby.freqs)) sorted.moby.rel.freqs[1:10] plot(sorted.moby.rel.freqs[1:10], type="b", xlab="Top Ten Words In Moby Dick by Rel Freq", ylab="Percentage of Full Text", xaxt="n") axis(1, 1:10, labels=names(sorted.moby.rel.freqs[1:10])) ############################################################### # BREAK TIME: (10:15-10:30) ############################################################### ############################################################### # SESSION TWO (10:30-12:00) ############################################# # 2.1 Downloading and exploring the exercise corpus ############################################# # http://www.matthewjockers.net/wp-content/uploads/2013/04/uwm-workshop.zip # setwd or Session->Set Working Dir->Choose Dir setwd("~/Documents/Workshops/Wisconsin/uwm-workshop") # Load a directory of files for analysis corpusDir<-"data/corpus" files<-dir(path=corpusDir, pattern=".*txt") files #explain what we are seeing here # Load and examine the metadata file metadata<-read.csv("data/metadata.csv", stringsAsFactors=F) dim(metadata) class(metadata) head(metadata) # what is a data.frame? # show how a dataframe is accessed metadata[1:2, 1:4] # show short cut for column names metadata$author metadata[,4] #show colnames function colnames(metadata) # Summary: So we have a vector of file names that we sucked out of a directory and we have a data.frame that we loaded from a .csv file (e.g. an excel file) that contains metadata about the files in the directory. We now want to create a topic model of all the files in that directory and we then want to be able to say something interesting about the topics in this corpus. ############################################# # 2.2 What is Latent Dirichlet Allocation (LDA) anyhow? ############################################# # LDA buffet story. ############################################# # 2.3 Text Chunking ############################################# # load a text from the directory--Dorian Gray novel.lines<-scan(file.path(corpusDir, files[33]), what="character", sep="\n") novel<-paste(novel.lines, collapse=" ") novel.lower <-tolower(novel) wilde.words<-strsplit(novel.lower, "\\W") # tokenize wilde.words.vector<-unlist(wilde.words) not.blanks<- which(wilde.words.vector!="") # remove blanks wilde.words.vector<- wilde.words.vector[not.blanks] length(wilde.words.vector) # [1] 80294 # We want to chunk this into n 1000 word chunks. chunk.size<-1000 # set a chunk variable num.chunks<-length(wilde.words.vector)/chunk.size num.chunks x <- seq_along(wilde.words.vector) # create a list where each item is a chunk vector chunks <- split(wilde.words.vector, ceiling(x/chunk.size)) # What have we done here? ceiling is a rounding function. ? ceiling #see class(chunks) #inspect the first 1500 values ceiling(x/chunk.size)[1:1500] # what about split ? split #examine the result str(chunks) # so now we need to convert this list of vectors into a list of contiguous text chunks # so consider that this first chunk chunks[[1]] # contains a vector of words from the first 1000 words chunk. But instead of a vector, we need a string of continues words, like this: paste(chunks[[1]], collapse=" ") ############################################# # 2.4 lapply ############################################# # we can apply this paste function over and over again using lapply, a special function for iterating over list items. # let's learn lapply. # lapply is a function that takes a list as an argument (i.e. our chunks list) # and then returns a new list having applied a function to each item in the list chunks.as.strings<-lapply(chunks, paste, collapse=" ") # here we use lappy to apply the "paste" function to each item in the list and we provide the additional collapse argument and an optional argument to the function # so now this chunks.as.strings[[1]] # is the same as this: paste(chunks[[1]], collapse=" ") # but we also have chunks.as.strings[[2]] # and so on. # so now we have a list of text chunks as strings of text. # we want to send these to a topic model but before we can do that we have to get them out of the list format chunk.vector<-unlist(chunks.as.strings) length(chunk.vector) ############################################# # 2.5 A Toy Model ############################################# # Now we will run a topic model just for fun # Let's see if we can find 5 topics in Dorian Gray # I'll explain all of this later, so just play along for now. library(lda) doclines<-lexicalize(chunk.vector) set.seed(8675309) K <- 5 num.iterations<-250 result <- lda.collapsed.gibbs.sampler(doclines$documents, K, doclines$vocab, num.iterations, 0.1, 0.1, compute.log.likelihood=TRUE) top.words <- top.topic.words(result$topics, 25, by.score=TRUE) # let's see what we've got top.words # tada, it's garbage! The problem is that we have lot's of high frequency words that # are screwing up the topical-ness of our topic model. # so we need to get rid of these danged stop words. ############################################# # 2.6 What is a stoplist ############################################# # Here is a typical stop list english.stoplist<-read.csv("/Users/mjockers/Documents/Workshops/Wisconsin/data/english.stop.txt", stringsAsFactors=F) english.stoplist<-unique(english.stoplist[,1]) # our goal is to scan each chunk and remove words that are in the stoplist. # remember chunks? str(chunks) # it is a list containing a series of word vectors. # we need to go back to the point just before we glued all of those words in each vector together with the following command # chunks.as.strings<-lapply(chunks, paste, collapse=" ") # before gluing in all together, we need to remove the stopwords from each chunk # we can use lapply again here too, but we need a custom function to apply ############################################# # 2.7 A Function ############################################# # Explain this function mywhich<-function(word.vector, stoplist){ word.vector[-which(word.vector %in% stoplist)] } # Now apply the function over each item in the chunks list chunks.without.stops<-lapply(chunks, mywhich, english.stoplist) #now we need to collapse the chunks just like in the past chunks.as.strings<-lapply(chunks.without.stops, paste, collapse=" ") # and convert to a text cector from a list chunk.vector<-unlist(chunks.as.strings) length(chunk.vector) ############################################# # 2.8 A Toy Model again ############################################# # Now run the LDA again, over the new list # Let's see if we can find 5 topics in Dorian Gray # I'll explain all of this later, so just play along for now. library(lda) doclines<-lexicalize(chunk.vector) set.seed(8675309) K <- 5 num.iterations<-250 result <- lda.collapsed.gibbs.sampler(doclines$documents, K, doclines$vocab, num.iterations, 0.1, 0.1, compute.log.likelihood=TRUE) top.words <- top.topic.words(result$topics, 25, by.score=TRUE) # let's see what we've got top.words # So, these topics are better, but still not the best we could hope for due # to the presence of a lots of character names. ########################################################################### # LUNCH BREAK (12:00-1:30) ########################################################################### ########################################################################### # SESSION 3 (1:30-2:45) ############################################# # 3.1 A better stoplist ############################################# # lets load a much larger stoplist that has character names long.stoplist<-read.csv("data/stoplist.csv", stringsAsFactors=F) long.stoplist<-unique(long.stoplist[,1]) length(long.stoplist) ############################################# # 3.2 A slightly better model ############################################# chunks.without.stops<-lapply(chunks, mywhich, long.stoplist) #now we need to collapse the chunks just like in the past chunks.as.strings<-lapply(chunks.without.stops, paste, collapse=" ") # and convert to a text cector from a list chunk.vector<-unlist(chunks.as.strings) library(lda) doclines<-lexicalize(chunk.vector) set.seed(8675309) K <- 10 num.iterations<-250 result <- lda.collapsed.gibbs.sampler(doclines$documents, K, doclines$vocab, num.iterations, 0.1, 0.1, compute.log.likelihood=TRUE) top.words <- top.topic.words(result$topics, 25, by.score=TRUE) # let's see what we've got top.words # Well, this is getting better and maybe at the level of a single novel # we might even want to keep the character names in. But once we move # from a single text to a corpus, we really don't want character based topics. # visit # http://www.matthewjockers.net/2013/04/12/secret-recipe-for-topic-modeling-themes/ # to show how topics w just english stopwords have lots of character names. # explain why this is a problem ############################################# # 3.3 POS tagging ############################################# novel.lines<-scan(file.path(corpusDir, files[33]), what="character", sep="\n") text.blob<-paste(novel.lines, collapse=" ") library(openNLP) tagged_text <- tagPOS(text.blob) # takes 2 minutes # need a new function tagged.words<-unlist(strsplit(tagged_text," ")) # Now chunk the full list chunk.size<-1000 # set a chunk variable num.chunks<-length(tagged.words)/chunk.size x <- seq_along(tagged.words) # create a list where each item is a chunk vector chunks <- split(tagged.words, ceiling(x/chunk.size)) ############################################# # 3.4 new functions ############################################# # New Function SelectTaggedWords <- function(Words,tagID) { Words[ grep(tagID,Words) ] } tagged.words.select.list<-lapply(chunks, SelectTaggedWords, "/NN$") # New Function RemoveTags <- function(Words) { sub("/[A-Z]{2,3}","",Words) } words.select.list<-lapply(tagged.words.select.list, RemoveTags) # New Function RemoveNonChar <- function(Words) { gsub("[^[:alnum:][:space:]']","",Words) } text.word.vector <- lapply(words.select.list, RemoveNonChar) chunks.without.stops<-lapply(text.word.vector, mywhich, english.stoplist) ############################################# # 3.5 re-modeling ############################################# #now we need to collapse the chunks just like in the past chunks.as.strings<-lapply(chunks.without.stops, paste, collapse=" ") # and convert to a text cector from a list chunk.vector<-unlist(chunks.as.strings) library(lda) doclines<-lexicalize(chunk.vector) set.seed(8675309) K <- 20 num.iterations<-250 result <- lda.collapsed.gibbs.sampler(doclines$documents, K, doclines$vocab, num.iterations, 0.1, 0.1, compute.log.likelihood=TRUE) top.words <- top.topic.words(result$topics, 5, by.score=TRUE) # let's see what we've got top.words # [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] # [1,] "pleasure" "lamp" "gold" "hansom" "murder" "experience" "companion" "bag" # [2,] "worship" "women" "silver" "horse" "crime" "art" "grass" "train" # [3,] "one" "street" "silk" "driver" "laboratory" "impulse" "wind" "coat" # [4,] "coffee" "curtain" "ceiling" "lace" "action" "instinct" "daisy" "servant" # [5,] "spirit" "ice" "damask" "cell" "method" "nothing" "happiness" "uncle" # [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] # [1,] "art" "blood" "love" "life" "sir" "music" "accident" "mother" # [2,] "artist" "cap" "theatre" "man" "picture" "principle" "sort" "son" # [3,] "painter" "poison" "night" "thing" "screen" "advantage" "town" "gentleman" # [4,] "picture" "race" "act" "time" "key" "carpet" "fellow" "marriage" # [5,] "fellow" "collar" "voice" "room" "tea" "host" "omen" "child" # [,17] [,18] [,19] [,20] # [1,] "book" "picture" "world" "husband" # [2,] "type" "canvas" "consolation" "tragedy" # [3,] "paper" "portrait" "yesterday" "dear" # [4,] "music" "prayer" "globe" "woman" # [5,] "background" "mouth" "caprice" "lady" ############################################################### # BREAK (2:45-3:00) ############################################################### ############################################################### # SESSION 4 (3:00-4:30) ############################################################### # now let's switch to a pre POS tagged corpus and make thematic clouds # This Requires POS tagging all of the texts. Sicne this takes several hours # i did it in advance. Here is the code required to do it on your own # library(openNLP) # for(i in 13:length(files)){ # text<-scan(file.path(corpusDir, files[i]), what="character", sep="\n") # text.blob<-paste(text, collapse=" ") # tagged_text <- tagPOS(text.blob) # write(tagged_text, paste("data/taggedCorpus/",files[i], sep="")) # } ############################################################### ############################################# # 4.1 ingesting the entire corpus ############################################# inputDir<-"data/taggedCorpus" files<-dir(path=inputDir, pattern=".*txt") chunk.size<-1000 #number of words per chunk long.stoplist<-read.csv("data/stoplist.csv", stringsAsFactors=F) long.stoplist<-unique(long.stoplist[,1]) # Let's take all of the functions we wrote above and move them into a # new file called pos.functions.r # once they are in this other file, we can call them up using the "source" function. source("code/pos.functions.r") topic.df<-NULL for(i in 1:length(files)){ tagged_text<-scan(file.path(inputDir, files[i]), what="character", sep="\n") tagged_words <- unlist(strsplit(tagged_text," ")) tagged_words <- c(SelectTaggedWords(tagged_words,"/NN$")) # SELECT POS TAG TYPES TO KEEP tagged_words_less <- RemoveTags(tagged_words) word.vector <- RemoveNonChar(tagged_words_less) #remove the blanks text.word.vector<- word.vector[which(word.vector!="")] #remove stopwords text.word.vector<-text.word.vector[-which(text.word.vector %in% long.stoplist)] num.chunks<-length(text.word.vector)/chunk.size max<-length(text.word.vector)/num.chunks x <- seq_along(text.word.vector) chunks <- split(text.word.vector, ceiling(x/max)) # Here I introduce a way to deal with small chunks at the end if(length(chunks[[length(chunks)]]) <= 500){ chunks[[length(chunks)-1]]<-c(chunks[[length(chunks)-1]], chunks[[length(chunks)]]) chunks[[length(chunks)]]<-NULL } chunks<-lapply(chunks, paste, collapse=" ") chunks.df<-do.call(rbind, chunks) chunks.df<-cbind(files[i],chunks.df) topic.df<-rbind(topic.df, chunks.df) } ############################################# # 4.2 Modeling the entire corpus ############################################# library(lda) doclines<-lexicalize(topic.df[,2]) set.seed(8675309) K <- 50 num.iterations<-250 result <- lda.collapsed.gibbs.sampler(doclines$documents, K, doclines$vocab, num.iterations, 0.1, 0.1, compute.log.likelihood=TRUE) top.words <- top.topic.words(result$topics, 10, by.score=TRUE) # View top words top.words ############################################# # 4.3 Visualizing topics as clouds ############################################# library(wordcloud) result.dir<-"topicCloudsFromTagged/" for(i in 1:nrow(result$topics)){ cloud.data<-sort(result$topics[i,], decreasing=T)[1:50] png(paste(result.dir, i,".png", sep=""), width = 12, height = 12, units="in", res=300) print(wordcloud(names(cloud.data), freq=cloud.data, scale=c(4,.10), min.freq=1, rot.per=0, random.order=F)) dev.off() } ############################################# # 4.4 Topic data analysis ############################################# # after looking at the clouds, we decide we are interested in # topic 25 which has to do with depictions of slavery # First let's get the proportional data from the model topic.proportions <- t(result$document_sums) / colSums(result$document_sums) # topic.proportions is a matrix with 932 rows: one for each 1000 word text segment # and 50 columns, one for each topic. To make life easier, let's create a column header row colnames(topic.proportions)<-paste("Topic",1:50, sep="_") # But there is no info in the matrix about which file chunk is which. # but we have that data in the first column of topic.df and we can get it out like this thefilenames<-topic.df[, 1] # so now we need to assign each chunk a unique number chunk.ids<-ave(final.df[,3], final.df$thefilenames, FUN = seq_along) # we can then "bind" all of this "column" data # to the the topic.proportions matrix, like this final.df<-cbind.data.frame(thefilenames, chunk.ids, topic.proportions) # show the result like this. . . . final.df[1:25, 1:5] # now the file names can act like "keys" in a relational database and allow us to lookup the metadata about those files in the metadata table we loaded earlier. for example, metadata<-read.csv("data/metadata.for.tagged.csv", stringsAsFactors=F) # but first we need to fix the file extensions since we switched to the .txt files metadata$filename<-gsub(".xml", ".txt", metadata$filename) # and now we can figure out which files are which using "which" metadata[which(metadata$filename == "1008.txt"), ] # so let's say we are interested in the slavery topic, topic 25. # We can identify the book with the largest use of the theme like this max.slavery<-final.df[which(final.df$Topic_25 == max(final.df$Topic_25)), "thefilenames"] # so, which book is it? metadata[which(metadata$filename == max.slavery),] # now let's plot the presence of slavery across the entire novel plot(final.df[which(final.df$thefilenames == max.slavery), "Topic_25"], type="l") # and finally, let's calculate and plot the mean usage of the theme across all the novels in the corpus. ############################################# # 4.5 Basic Plotting ############################################# mean.slavery<-aggregate(final.df$Topic_25, by=list(thefilenames), mean) #plot it barplot(mean.slavery$x,names.arg=mean.slavery$Group.1, las=2) # now export the image. this makes us wonder about text 500.txt metadata[which(metadata$filename == "500.txt"),] # X filename date author title # 41 41 500.txt 1867 Child, Lydia Maria Francis A Romance of the Republic. (1867) # nation # 41 American |