Text mining in R. A different approach to The Iliad

Posted on October 15, 2018 by Assorted things in R bloggers | 0 Comments

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Introduction

This project is an attempt to get familiarity with text mining in R and I haven’t found a better way to get it that mining The Iliad. This familiarity with text mining can be very useful because “much of the data proliferating today is unstructured and text-heavy.”¹

I’ve chosen the tidytext ² approach to text minig in order to test if it’s so effective as the tidy familiy.

Wordclouds

The first step in the process of mining The Iliad is to know which words are more frequent in each book. The Iliad is divided in 24 books. As a very basic learner of ancient greek I find interesting to do this using the classic greek version of the book.

So I used Perseus Digital Library ³ catalog to access the original classic greek text of The Iliad in XML version. As every classic greek learner should know this is a declinated language so the same word can appear in very different forms. To achieve a realistic count of every single greek word I’ve used Greek Word Study Tool ⁴ to find the primal lemma of the declined word

Let’s see the code:

library(XML)
library(wordcloud)
library(RCurl)
library(httr)
library(RColorBrewer)

GetXmlChapter <- function(chapter=1,tipo="noun"){
  if(file.exists(paste0("chapter",chapter,".rds"))==FALSE){
    path <- "http://www.perseus.tufts.edu/hopper/xmlchunk?doc=Perseus%3Atext%3A1999.01.0133%3Abook%3D"
    path.c <- paste0(path,chapter)
    chapter.xml <- xmlParse(path.c)
    xml_data <- xmlToList(chapter.xml)
    nlineas <- length(xml_data$text$body$div1)
    lineas <- list()
    l <- 1
    for (i in 1:length(xml_data$text$body$div1)){
      lineas[[l]] <- LeeChunck(xml_data$text$body$div1[i])
      #print(LeeChunck(xml_data$text$body$div1[i]))
      l <- l+1
    }
    lineas <- unlist(lineas)
    #quitamos las comas
    lineas <- lapply(lineas, function(x) gsub(",","",x))
    lineas <- lapply(lineas, function(x) gsub('/.',"",x))
    lineas <- lapply(lineas, function(x) gsub(";","",x))
    lineas <- lapply(lineas, function(x) gsub("?","",x))
    lineas <- lapply(lineas, function(x) gsub(":","",x))
    lineas <- lapply(lineas, function(x) strsplit(x," ",))
    palabras <- unlist(lineas)
    #palabras <- sample(palabras,100)
    def <-  vector(mode="character", length=length(palabras))
    tipo <-  vector(mode="character", length=length(palabras))
    
    for (i in 1:length(palabras)){
      print(sprintf("%s:Traduciendo %s",i,palabras[i]))
      res <- try(GetWord(palabras[i]))
      if(class(res) == "try-error"){
        print("sleep an try again")
        Sys.sleep(1)
        res <- try(GetWord(palabras[i]))
        if(class(res) == "try-error"){
          print("sleep an try again")
          Sys.sleep(1)
          res <- try(GetWord(palabras[i]))
        }
      } 
      def[i] <- res[[1]]
      tipo[i] <- res[[2]]
    }
    res <- cbind(palabras,def,tipo)
    saveRDS(res,file = paste0("chapter",chapter,".rds"))
  }
  res <- readRDS(file = paste0("chapter",chapter,".rds") )
  res <- res[res[,3]==tipo,]
  summary <- as.data.frame(table(res[,2]))
  png(paste0("wordcloud_chapter",chapter,".png"), width=800, height=800, res=300)
  wordcloud(summary$Var1,summary$Freq,colors=brewer.pal(8, "Dark2"),random.order=FALSE,rot.per=0.35,scale=c(1.5,0.3), use.r.layout=FALSE,  max.words=100)
  dev.off()
  #return(g)
}

GetWord <- function(word){
  gc()
  if (word==""){
    return(list(NA,NA))
  }
  word.html <- NULL
  path <- sprintf("http://www.perseus.tufts.edu/hopper/morph?lang=greek&lookup=%s",word)
  #word.html <- htmlTreeParse(path,encoding = "UTF-8")
  while (is.null(word.html)){
    Sys.sleep(0.1)
    tabs <- GET(path)
    word.html <- htmlTreeParse(tabs,encoding = "UTF-8")
    if (!is.null(word.html$children)){
      if (grepl("503",word.html$children)){
          return(c(word,NA))
        }
      }
    }
  word.html <- xmlToList(word.html$children$html)
  
  if (word.html$body[2]$div[2]$div[2]$div[[1]]!="Sorry, no information was found for"){
    def <- word.html$body[2]$div[2]$div[2]$div[1]$div$div$div[3]
    if (!is.null(def)){
      tipo <- strsplit(word.html$body[2]$div[2]$div[2]$div$div$div[3]$table[2,1][[1]]," ")[[1]][1]
    } else {
      tipo <- NA
    }
    lemma <- word.html$body[2]$div[2]$div[2]$div[1]$div$div$div[1]
    if (class(lemma)=="list"){
      Encoding(lemma[[1]]) <- "UTF-8"
      return(list(lemma[[1]],tipo))
    } else {
      if(is.null(def)){
        return(list(word,NA))
      } else {
        Encoding(lemma) <- "UTF-8"
        return(list(lemma,tipo))
      }
    } 
  } else {
    print("Informacion no encontrada")
    return(list(NA,NA))
  }
  Encoding(lemma) <- "UTF-8"
  return(list(lemma,tipo))
}

LeeChunck <- function(chunck){
  lineas <- list()
  l <- 1
  #es una linea
  if (names(chunck)=="l"){
    #cin milestone
    if ("milestone" %in% names(chunck$l)){
      linea <- chunck$l$text
      lineas[[l]] <- linea
      l <- l+1
    } else {
      if ("text" %in% names(chunck$l)){
        linea <- chunck$l$text
        lineas[[l]] <- linea
        l <- l+1
      } else {
        linea <- chunck$l
        lineas[[l]] <- linea
        l <- l+1
      }
    }
  }
  # es un parrafo
  if (names(chunck)=="q"){
    #todos los chunkitos
    for (i in 1:length(chunck$q)){
      l2 <- LeeChunck(chunck$q[i])
      lineas[[l]] <- l2
      l <- l+1
    }
    
  }
  
  lineas <- unlist(lineas)
  return(lineas)
}

And let’s see some results

Text Mining

In this chapter I am replicating the analysys made in Text Mining with R ⁵ and aplying them to The Iliad.

Getting and cleaning the text

The dirty job, to this analysys the cleanest text of the book was needed. After a bit of web searching I’ve found in Gutenberg Project⁶ [this version] (http://www.gutenberg.org/cache/epub/16452/pg16452.txt), although this is the cleanest text I’ve found it’s is not clean at all; so you need a lot of cleaning.

library(dplyr)
library(tidytext)
library(tidyr)
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:reshape2':
## 
##     smiths
library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following object is masked from 'package:crayon':
## 
##     %+%
  con <- file("http://www.gutenberg.org/cache/epub/16452/pg16452.txt",open="r")
  lines <- readLines(con)
  lines.split <- vector("integer",24)
  for (book in 1:24){
    search <- sprintf("BOOK %s\\.",as.roman(book))
    lines.split[book] <- last(which(grepl(search,lines)==TRUE)) #porque la primera vez que aparece es el índice
  }
  final <- '                  \\*       \\*       \\*       \\*       \\*'
  libros <- vector("list",24)
  for (l in 1:24){
    if (l!=24){
      libros[[l]] <- lines[(lines.split[l]+1):(lines.split[(l+1)]-1)]
    } else {
      libros[[l]] <- lines[(lines.split[l]+1):length(lines)]
    }
    #hay final de linea
    if (TRUE %in% grepl(final,libros[[l]])){
      libros[[l]] <- libros[[l]][1:(which(grepl(final,libros[[l]])==TRUE)-1)]
    }
    #si hay ARGUMENT
    if (TRUE %in% grepl("ARGUMENT",libros[[l]])){
      libros[[l]] <- libros[[l]][1:(which(grepl("ARGUMENT",libros[[l]])==TRUE)-1)]
    }
  }
## Warning in 1:(which(grepl("ARGUMENT", libros[[l]]) == TRUE) - 1): numerical
## expression has 8 elements: only the first used
## Warning in 1:(which(grepl("ARGUMENT", libros[[l]]) == TRUE) - 1): numerical
## expression has 2 elements: only the first used
  #Vamos a limpiar todos los libros
  #Borro líneas vacías
  libros <- lapply(libros,FUN = function(x) x[x!=""])
  #Borro números
  libros <- lapply(libros,FUN = function(x) gsub('[0-9]+', '', x))
  #Borro lineas sueltas
  libros <- lapply(libros,FUN = function(x) x[!(grepl("THE ILIAD.",x))])
  libros <- lapply(libros,FUN = function(x) x[!(grepl("BOOK",x))])
  #Borro los corchetes de las notas
  libros <- lapply(libros,FUN = function(x) gsub('\\[|\\]+', '', x))
  #Borro cuando hay más de un espacio
  libros <- lapply(libros,FUN = function(x) gsub("\\s\\s+","",x))

After this hard cleaning job I get a list (one element for book) of vectors (one element for line).

head(libros[[1]])
## [1] "Achilles sing, O Goddess! Peleus' son;"     
## [2] "His wrath pernicious, who ten thousand woes"
## [3] "Caused to Achaia's host, sent many a soul"  
## [4] "Illustrious into Ades premature,"           
## [5] "And Heroes gave (so stood the will of Jove)"
## [6] "To dogs and to all ravening fowls a prey,"

The tidy text format: tidy text format breaks the text in individual tokens and transforms it to a tidy data structure using unnest_tokens().

library(tidytext)
library(formattable)
## 
## Attaching package: 'formattable'
## The following object is masked from 'package:crayon':
## 
##     style
libros.df <- lapply(libros,FUN=function(x) data.frame(line=1:length(x),text=x))
libros.df <- lapply(libros.df,FUN=function(x) x %>% unnest_tokens(word,text))
  for (i in 1:length(libros.df)){
    libros.df[[i]]$book <- i
  }
libros.df <- bind_rows(libros.df)
head(libros.df)
##   line     word book
## 1    1 achilles    1
## 2    1     sing    1
## 3    1        o    1
## 4    1  goddess    1
## 5    1   peleus    1
## 6    1      son    1

Word frequencies

freq <- libros.df %>% anti_join(stop_words) %>% group_by(book) %>% count(word,sort=TRUE) %>% group_by(book)
## Joining, by = "word"
freq <- split(freq,freq$book) 
freq10 <- lapply(freq, FUN=function(x) x[1:10,])

formattable(freq10[[1]])

book	word	n
1	thou	57
1	thy	55
1	thee	34
1	son	25
1	jove	24
1	achilles	22
1	host	20
1	gods	16
1	agamemnon	15
1	apollo	15

formattable(freq10[[2]])

book	word	n
2	ships	36
2	son	32
2	jove	29
2	thou	24
2	led	22
2	chief	20
2	troy	20
2	host	19
2	agamemnon	18
2	king	16

formattable(freq10[[3]])

book	word	n
3	thou	26
3	thy	19
3	paris	16
3	helen	15
3	thee	15
3	ye	14
3	troy	13
3	menelaus	12
3	menelaüs	12
3	fair	11

Sentiment analysis

As it is said in Text Mining with R “One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.”, probably a wrong method anyway.

sentiments <- libros.df %>% inner_join(get_sentiments("bing")) %>% count(book,index = line %/% 25, sentiment) %>% spread(sentiment, n, fill = 0) %>%  mutate(sentiment = positive - negative)
## Joining, by = "word"
ggplot(sentiments, aes(index, sentiment, fill = as.factor(book))) +
    geom_col(show.legend = FALSE) +
    facet_wrap(~book, ncol = 6, scales = "free_x") + labs(title="Sentiment analysis by words. The Iliad") + xlab("")

As can be seen the overall sentiment of the book is quite negative, for example book XXI is significantly negative. The summary of the chapter in Wikipedia is “Driving the Trojans before him, Achilles cuts off half their number in the river Skamandros and proceeds to slaughter them, filling the river with the dead. The river, angry at the killing, confronts Achilles but is beaten back by Hephaestus’ firestorm. The gods fight among themselves. The great gates of the city are opened to receive the fleeing Trojans, and Apollo leads Achilles away from the city by pretending to be a Trojan.”, can it bee positive?

Comparing the three sentiment dictionaries

libros.df$line2 <- 1:nrow(libros.df)
  afinn <- libros.df %>% 
    inner_join(get_sentiments("afinn")) %>% 
    group_by(index = line2 %/% 250) %>% 
    summarise(sentiment = sum(score)) %>% 
    mutate(method = "AFINN")
## Joining, by = "word"
  bing_and_nrc <- bind_rows(libros.df %>% 
                              inner_join(get_sentiments("bing")) %>%
                              mutate(method = "Bing et al."),
                            libros.df %>% 
                              inner_join(get_sentiments("nrc") %>% 
                                           dplyr::filter(sentiment %in% c("positive", 
                                                                   "negative"))) %>%
                              mutate(method = "NRC")) %>%
    count(method, index = line2 %/% 250, sentiment) %>%
    spread(sentiment, n, fill = 0) %>%
    mutate(sentiment = positive - negative)
## Joining, by = "word"
## Joining, by = "word"
  bind_rows(afinn, 
            bing_and_nrc) %>%
    ggplot(aes(index, sentiment, fill = method)) +
    geom_col(show.legend = FALSE) +
    facet_wrap(~method, ncol = 1, scales = "free_y")

The three sentiments sources are coherent.

Most common positive and negative words

bing_word_counts <- libros.df %>%
    inner_join(get_sentiments("bing")) %>%
    count(word, sentiment, sort = TRUE) %>%
    ungroup()
## Joining, by = "word"
  bing_word_counts %>%
    group_by(sentiment) %>%
    top_n(10) %>%
    ungroup() %>%
    mutate(word = reorder(word, n)) %>%
    ggplot(aes(word, n, fill = sentiment)) +
    geom_col(show.legend = FALSE) +
    facet_wrap(~sentiment, scales = "free_y") +
    labs(y = "Contribution to sentiment",
         x = NULL) +
    coord_flip()
## Selecting by n

The list of positive words is quite interesting with several words in the circle of ἀρετή: brave, noble, brigth, valiant, glorious.

Term frequency, Zipf’s law and bind_tf_idf function

book_words <- libros.df %>%
    count(book, word, sort = TRUE) %>%
    ungroup()
  
  total_words <- book_words %>% 
    group_by(book) %>% 
    summarize(total = sum(n))
  
  book_words <- left_join(book_words, total_words)
## Joining, by = "book"
  ggplot(book_words, aes(n/total, fill = as.factor(book))) +
    geom_histogram(show.legend = FALSE) +
    xlim(NA, 0.004) +
    facet_wrap(~book, ncol = 6, scales = "free_y")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 769 rows containing non-finite values (stat_bin).
## Warning: Removed 24 rows containing missing values (geom_bar).

  freq_by_rank <- book_words %>% 
    group_by(book) %>% 
    mutate(rank = row_number(), 
           `term frequency` = n/total)
  
  freq_by_rank %>% 
    ggplot(aes(rank, `term frequency`, color = as.factor(book))) + 
    geom_abline(intercept = -0.62, slope = -1.1, color = "gray50", linetype = 2) +
    geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) + 
    scale_x_log10() +
    scale_y_log10()

  rank_subset <- freq_by_rank %>% 
    dplyr::filter(rank < 500,
           rank > 10)
  
  lm(log10(`term frequency`) ~ log10(rank), data = rank_subset)
## 
## Call:
## lm(formula = log10(`term frequency`) ~ log10(rank), data = rank_subset)
## 
## Coefficients:
## (Intercept)  log10(rank)  
##     -1.0909      -0.8772
    book_words <- book_words %>%
    bind_tf_idf(word, book, n)
  book_words
## # A tibble: 41,939 x 7
##     book  word     n total         tf   idf tf_idf
##    <int> <chr> <int> <int>      <dbl> <dbl>  <dbl>
##  1    14   the   549  8234 0.06667476     0      0
##  2    23   the   547  8223 0.06652073     0      0
##  3    11   the   535  7757 0.06896996     0      0
##  4     2   the   494  7779 0.06350431     0      0
##  5    13   the   475  7420 0.06401617     0      0
##  6    16   the   462  7778 0.05939830     0      0
##  7     5   the   458  7956 0.05756662     0      0
##  8    17   the   424  6738 0.06292668     0      0
##  9    15   the   420  6773 0.06201093     0      0
## 10    24   the   399  7621 0.05235533     0      0
## # ... with 41,929 more rows
  book_words %>%
    select(-total) %>%
    arrange(desc(tf_idf))
## # A tibble: 41,939 x 6
##     book        word     n          tf       idf      tf_idf
##    <int>       <chr> <int>       <dbl>     <dbl>       <dbl>
##  1    10       dolon    16 0.003025147 3.1780538 0.009614079
##  2     3    menelaüs    12 0.002986560 3.1780538 0.009491450
##  3     6 bellerophon     8 0.001668405 3.1780538 0.005302280
##  4     3   alexander     8 0.001991040 2.0794415 0.004140252
##  5    13   deiphobus    14 0.001886792 2.0794415 0.003923475
##  6     7       idæus     9 0.002116153 1.7917595 0.003791638
##  7    24      litter     9 0.001180947 3.1780538 0.003753114
##  8     2       forty     9 0.001156961 3.1780538 0.003676884
##  9    16   patroclus    52 0.006685523 0.5389965 0.003603474
## 10     1    chrysëis     6 0.001049685 3.1780538 0.003335956
## # ... with 41,929 more rows
  book_words %>%
    arrange(desc(tf_idf)) %>%
    dplyr::filter(book<=4) %>% 
    mutate(word = factor(word, levels = rev(unique(word)))) %>% 
    group_by(book) %>% 
    top_n(15) %>% 
    ungroup %>%
    ggplot(aes(word, tf_idf, fill = as.factor(book))) +
    geom_col(show.legend = FALSE) +
    labs(x = NULL, y = "tf-idf",title="tf-idf for book I to IV") +
    facet_wrap(~book, ncol = 2, scales = "free") +
    coord_flip()
## Selecting by tf_idf

Relations between words

Bigrams

  libros.df_2 <- lapply(libros,FUN=function(x) data.frame(line=1:length(x),text=x))
  libros.df_2 <- lapply(libros.df_2,FUN=function(x) x %>% unnest_tokens(bigram,text,token="ngrams",n=2))
  for (i in 1:length(libros.df_2)){
    libros.df_2[[i]]$book <- i
  }
  iliad_bigrams <- bind_rows(libros.df_2)
  
  iliad_bigrams %>%
    count(bigram, sort = TRUE)
## # A tibble: 64,434 x 2
##      bigram     n
##       <chr> <int>
##  1   of the   574
##  2   to the   459
##  3   on the   353
##  4  and the   329
##  5   in the   319
##  6 from the   305
##  7   of all   212
##  8  all the   206
##  9   son of   205
## 10   to his   205
## # ... with 64,424 more rows
  bigrams_separated <- iliad_bigrams %>%
    separate(bigram, c("word1", "word2"), sep = " ")
  
  bigrams_filtered <- bigrams_separated %>%
    dplyr::filter(!word1 %in% stop_words$word) %>%
    dplyr::filter(!word2 %in% stop_words$word)
  
  head(bigrams_filtered,10)
##    line    word1      word2 book
## 1     1 achilles       sing    1
## 2     1  goddess     peleus    1
## 3     1   peleus        son    1
## 4     2    wrath pernicious    1
## 5     2      ten   thousand    1
## 6     2 thousand       woes    1
## 7     3 achaia's       host    1
## 8     4     ades  premature    1
## 9     6 ravening      fowls    1
## 10    7   fierce    dispute    1

Trigrams

  libros.df_3 <- lapply(libros,FUN=function(x) data.frame(line=1:length(x),text=x))
  libros.df_3 <- lapply(libros.df_3,FUN=function(x) x %>% unnest_tokens(trigram,text,token="ngrams",n=3))
  for (i in 1:length(libros.df_3)){
    libros.df_3[[i]]$book <- i
  }
  iliad_trigrams <- bind_rows(libros.df_3)
  iliad_trigrams %>% separate(trigram, c("word1", "word2", "word3"), sep = " ") %>%
    dplyr::filter(!word1 %in% stop_words$word,
           !word2 %in% stop_words$word,
           !word3 %in% stop_words$word) %>%
    count(word1, word2, word3, sort = TRUE)
## # A tibble: 5,727 x 4
##      word1     word2  word3     n
##      <chr>     <chr>  <chr> <int>
##  1 laertes     noble    son     7
##  2    wind     swept  ilium     7
##  3    blue      eyed pallas     6
##  4    gore   tainted   mars     6
##  5    jove      ægis  arm'd     6
##  6    <NA>      <NA>   <NA>     6
##  7   close  fighting   sons     5
##  8  atreus    mighty    son     4
##  9   cloud assembler    god     4
## 10   cloud assembler   jove     4
## # ... with 5,717 more rows
  head(iliad_trigrams,10)
##    line              trigram book
## 1     1      achilles sing o    1
## 2     1       sing o goddess    1
## 3     1     o goddess peleus    1
## 4     1   goddess peleus son    1
## 5     2 his wrath pernicious    1
## 6     2 wrath pernicious who    1
## 7     2   pernicious who ten    1
## 8     2     who ten thousand    1
## 9     2    ten thousand woes    1
## 10    3   caused to achaia's    1

Everyone who has read The Iliad knows about the repetitions in the text (supposedly due to oral transmision), we can show this here:

 bigrams_filtered %>%
    dplyr::filter(word2 == "god") %>%
    count(word1, sort = TRUE)
## # A tibble: 14 x 2
##            word1     n
##            <chr> <int>
##  1        archer     5
##  2     assembler     4
##  3       warrior     3
##  4         angry     1
##  5        bender     1
##  6        coming     1
##  7      guardian     1
##  8      immortal     1
##  9 indefatigable     1
## 10          jove     1
## 11        mighty     1
## 12      stirring     1
## 13           thy     1
## 14       tossing     1
  bigrams_filtered %>%
    dplyr::filter(word2 == "achilles") %>%
    count(word1, sort = TRUE)
## # A tibble: 94 x 2
##        word1     n
##        <chr> <int>
##  1     swift    11
##  2     brave     7
##  3   godlike     5
##  4  renown'd     5
##  5    divine     4
##  6 myrmidons     4
##  7     noble     4
##  8     spake     3
##  9      thou     3
## 10  upsprang     3
## # ... with 84 more rows
  bigram_tf_idf <- iliad_bigrams %>%
    count(book, bigram) %>%
    bind_tf_idf(bigram, book, n) %>%
    arrange(desc(tf_idf))
  
  bigram_tf_idf %>% arrange(desc(tf_idf)) %>%
    dplyr::filter(book<=4) %>% 
    mutate(bigram = factor(bigram, levels = rev(unique(bigram)))) %>% 
    group_by(book) %>% 
    top_n(15) %>% 
    ungroup %>%
    ggplot(aes(bigram, tf_idf, fill = as.factor(book))) +
    geom_col(show.legend = FALSE) +
    labs(x = NULL, y = "tf-idf") +
    facet_wrap(~book, ncol = 4, scales = "free") +
    coord_flip()
## Selecting by tf_idf

Text Mining with R https://www.tidytextmining.com/index.html ↩
Tidytext https://github.com/juliasilge/tidytext ↩
Perseus Digital Library http://www.perseus.tufts.edu/hopper/↩
Perseus Digital Library Greek Word Study Tool http://www.perseus.tufts.edu/hopper/morph?lang=greek&lookup=%E1%BC%A1 ↩
Project Gutenberg http://www.gutenberg.org/↩
Project Gutenberg http://www.gutenberg.org/↩

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Text mining in R. A different approach to The Iliad

Introduction

Wordclouds

Text Mining

Getting and cleaning the text

Word frequencies

Sentiment analysis

Comparing the three sentiment dictionaries

Most common positive and negative words

Term frequency, Zipf’s law and bind_tf_idf function

Relations between words

Bigrams

Trigrams

Related

Introduction

Wordclouds

Text Mining

Getting and cleaning the text

Word frequencies

Sentiment analysis

Comparing the three sentiment dictionaries

Most common positive and negative words

Term frequency, Zipf’s law and bind_tf_idf function

Relations between words

Bigrams

Trigrams

Related

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