Giter Site home page Giter Site logo

vocabular2's Introduction

vocabular2

Lifecycle: experimental

The goal of vocabular2 is to compare vocabularies on a set of metrics. There’s currently no clear development path for the package. It may become usable in the future, but for now it’s not adviced to use the code for your projects. I haven’t spent enough time thinking about the meaningfulness of the metrics to recommend them. They were simply intuitive to me at 4am on some exam-stressed winter night. It’s also very possible that they are in the literature under different names. :)

Installation

You can install the development version with:

devtools::install_github("ludvigolsen/vocabular2")

Main functions

  • compare_vocabs()
  • get_doc_metrics()
  • stack_doc_metrics()

Simple Example

Note: By default, negative values are set to 0 for most of the metrics (not TD-IDF and TF-IRF).

See the metric formulas below the example.

Attach packages

library(vocabular2)
library(tm)
library(tidyverse)
library(knitr)

Load the included ‘hamlet’ dataset

# The included dataset with Hamlet lines
# Extracted from https://www.opensourceshakespeare.org/
hamlet %>% head(5)
#> # A tibble: 5 x 2
#>   Line                                              Character
#>   <chr>                                             <chr>    
#> 1 Though yet of Hamlet our dear brother's death     Claudius 
#> 2 The memory be green, and that it us befitted...   Claudius 
#> 3 We doubt it nothing. Heartily farewell.           Claudius 
#> 4 Have you your father's leave? What says Polonius? Claudius 
#> 5 Take thy fair hour, Laertes. Time be thine,       Claudius

# Collect the lines for each character
data <- hamlet %>% 
  dplyr::group_by(Character) %>% 
  dplyr::summarise(txt = paste0(Line, collapse = " "))

data
#> # A tibble: 5 x 2
#>   Character txt                                                                 
#>   <chr>     <chr>                                                               
#> 1 Claudius  Though yet of Hamlet our dear brother's death The memory be green, …
#> 2 Gertrude  Good Hamlet, cast thy nighted colour off, And let thine eye look li…
#> 3 Hamlet    Not so, my lord. I am too much i' th' sun. Ay, madam, it is common.…
#> 4 Horatio   Friends to this ground. A piece of him. Tush, tush, 'twill not appe…
#> 5 Ophelia   Do you doubt that? No more but so? I shall th' effect of this good …

# Assign each text to a variable
# This could be done in a loop if we had a lot of texts
claudius <- data[1, "txt"][[1]]
gertrude <- data[2, "txt"][[1]]
hamlet <- data[3, "txt"][[1]] # note: overwrites the dataset
horatio <- data[4, "txt"][[1]]
ophelia <- data[5, "txt"][[1]]

Count the terms

# Create a term-count tibble for each document

count_terms <- function(t){
  docs <- Corpus(VectorSource(t))
  # do things like removing stopwords, lemmatization, etc.
  docs <- tm_map(docs, removeWords, stopwords("english"))
  docs <- tm_map(docs, removePunctuation, preserve_intra_word_dashes = TRUE)
  dtm <- TermDocumentMatrix(docs)
  m <- as.matrix(dtm)
  v <- sort(rowSums(m), decreasing=TRUE)
  d <- tibble::tibble(Word = names(v), Count=v)
  d
}

claudius_tc <- count_terms(claudius)
gertrude_tc <- count_terms(gertrude)
hamlet_tc <- count_terms(hamlet)
horatio_tc <- count_terms(horatio)
ophelia_tc <- count_terms(ophelia)

Compare the vocabularies

This is where the metrics are calculated. We get a column per document with a nested tibble containing the metrics.

scores <- compare_vocabs(tc_dfs = list("claudius" = claudius_tc,
                                       "gertrude" = gertrude_tc,
                                       "hamlet" = hamlet_tc,
                                       "horatio" = horatio_tc,
                                       "ophelia" = ophelia_tc))
scores
#> # A tibble: 887 x 7
#>    Word     `In Docs` claudius     gertrude     hamlet     horatio    ophelia   
#>    <chr>        <dbl> <list>       <list>       <list>     <list>     <list>    
#>  1 ability          1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#>  2 aboard           1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#>  3 acquitt…         1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#>  4 act              1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#>  5 admirat…         1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#>  6 affecti…         1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#>  7 affecti…         1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#>  8 affrigh…         1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#>  9 aha              1 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#> 10 air              2 <tibble [1 … <tibble [1 … <tibble [… <tibble [… <tibble […
#> # … with 877 more rows

Extract the metrics for Claudius

get_doc_metrics(scores, "claudius") %>% 
  arrange(desc(REL_TF_NRTF)) %>% 
  head(10) %>% 
  kable()
Doc Word In Docs Count TF IRF RTF NRTF MRTF TF_IDF TF_IRF TF_RTF TF_NRTF TF_MRTF REL_TF_NRTF REL_TF_MRTF RANK_ENS
claudius give 2 7 0.0132075 0.6931472 0.0022472 0.0005618 0.0022472 0.0067468 0.0091548 0.0109604 0.0126457 0.0109604 0.1314895 0.0490369 886.0
claudius gertrude 1 5 0.0094340 1.3862944 0.0000000 0.0000000 0.0000000 0.0086443 0.0130782 0.0094340 0.0094340 0.0094340 0.1255340 0.1255340 885.0
claudius laertes 2 9 0.0169811 0.6931472 0.0059880 0.0014970 0.0059880 0.0086744 0.0117704 0.0109931 0.0154841 0.0109931 0.1193114 0.0279667 887.0
claudius leave 1 3 0.0056604 1.3862944 0.0000000 0.0000000 0.0000000 0.0051866 0.0078469 0.0056604 0.0056604 0.0056604 0.0451922 0.0451922 883.5
claudius polonius 1 3 0.0056604 1.3862944 0.0000000 0.0000000 0.0000000 0.0051866 0.0078469 0.0056604 0.0056604 0.0056604 0.0451922 0.0451922 883.5
claudius hamlet 3 15 0.0283019 0.2876821 0.0428010 0.0107002 0.0359281 0.0063154 0.0081419 0.0000000 0.0176016 0.0000000 0.0439106 0.0000000 673.0
claudius time 2 4 0.0075472 0.6931472 0.0022472 0.0005618 0.0022472 0.0038553 0.0052313 0.0053000 0.0069854 0.0053000 0.0415048 0.0135499 882.0
claudius father 4 6 0.0113208 0.0000000 0.0081824 0.0020456 0.0029940 0.0000000 0.0000000 0.0031384 0.0092752 0.0083267 0.0381682 0.0255019 697.0
claudius thine 2 4 0.0075472 0.6931472 0.0029940 0.0007485 0.0029940 0.0038553 0.0052313 0.0045532 0.0067987 0.0045532 0.0352249 0.0092965 881.0
claudius must 3 6 0.0113208 0.2876821 0.0091200 0.0022800 0.0068729 0.0025262 0.0032568 0.0022007 0.0090407 0.0044479 0.0342901 0.0066663 880.0

Extract the metrics for Gertrude

get_doc_metrics(scores, "gertrude") %>% 
  arrange(desc(REL_TF_NRTF)) %>% 
  head(10) %>% 
  kable()
Doc Word In Docs Count TF IRF RTF NRTF MRTF TF_IDF TF_IRF TF_RTF TF_NRTF TF_MRTF REL_TF_NRTF REL_TF_MRTF RANK_ENS
gertrude drownd 1 3 0.0089820 1.3862944 0.0000000 0.0000000 0.0000000 0.0082302 0.0124517 0.0089820 0.0089820 0.0089820 0.1296075 0.1296075 887.0
gertrude hamlet 3 12 0.0359281 0.2876821 0.0351747 0.0087937 0.0283019 0.0080171 0.0103359 0.0007534 0.0271345 0.0076263 0.1040184 0.0096092 875.0
gertrude thou 4 8 0.0239521 0.0000000 0.0219195 0.0054799 0.0117647 0.0000000 0.0000000 0.0020326 0.0184722 0.0121874 0.0727235 0.0237111 730.0
gertrude hast 2 3 0.0089820 0.6931472 0.0018868 0.0004717 0.0018868 0.0045883 0.0062259 0.0070952 0.0085103 0.0070952 0.0698872 0.0254277 885.5
gertrude ophelia 2 3 0.0089820 0.6931472 0.0018868 0.0004717 0.0018868 0.0045883 0.0062259 0.0070952 0.0085103 0.0070952 0.0698872 0.0254277 885.5
gertrude thy 3 6 0.0179641 0.2876821 0.0135679 0.0033920 0.0113208 0.0040086 0.0051679 0.0043961 0.0145721 0.0066433 0.0653355 0.0100518 883.0
gertrude this 2 3 0.0089820 0.6931472 0.0022472 0.0005618 0.0022472 0.0045883 0.0062259 0.0067348 0.0084202 0.0067348 0.0638903 0.0211089 884.0
gertrude alack 1 2 0.0059880 1.3862944 0.0000000 0.0000000 0.0000000 0.0054868 0.0083012 0.0059880 0.0059880 0.0059880 0.0576034 0.0576034 881.0
gertrude forgot 1 2 0.0059880 1.3862944 0.0000000 0.0000000 0.0000000 0.0054868 0.0083012 0.0059880 0.0059880 0.0059880 0.0576034 0.0576034 881.0
gertrude noise 1 2 0.0059880 1.3862944 0.0000000 0.0000000 0.0000000 0.0054868 0.0083012 0.0059880 0.0059880 0.0059880 0.0576034 0.0576034 881.0

Extract the metrics for Hamlet

get_doc_metrics(scores, "hamlet") %>% 
  arrange(desc(REL_TF_NRTF)) %>% 
  head(10) %>% 
  kable()
Doc Word In Docs Count TF IRF RTF NRTF MRTF TF_IDF TF_IRF TF_RTF TF_NRTF TF_MRTF REL_TF_NRTF REL_TF_MRTF RANK_ENS
hamlet hold 1 4 0.0117647 1.3862944 0.0000000 0.0000000 0.0000000 0.0107799 0.0163093 0.0117647 0.0117647 0.0117647 0.2215225 0.2215225 887
hamlet horatio 2 5 0.0147059 0.6931472 0.0018868 0.0004717 0.0018868 0.0075121 0.0101933 0.0128191 0.0142342 0.0128191 0.1909742 0.0751466 886
hamlet horrible 1 3 0.0088235 1.3862944 0.0000000 0.0000000 0.0000000 0.0080849 0.0122320 0.0088235 0.0088235 0.0088235 0.1246064 0.1246064 885
hamlet boy 1 2 0.0058824 1.3862944 0.0000000 0.0000000 0.0000000 0.0053899 0.0081547 0.0058824 0.0058824 0.0058824 0.0553806 0.0553806 882
hamlet earth 1 2 0.0058824 1.3862944 0.0000000 0.0000000 0.0000000 0.0053899 0.0081547 0.0058824 0.0058824 0.0058824 0.0553806 0.0553806 882
hamlet fellow 1 2 0.0058824 1.3862944 0.0000000 0.0000000 0.0000000 0.0053899 0.0081547 0.0058824 0.0058824 0.0058824 0.0553806 0.0553806 882
hamlet hell 1 2 0.0058824 1.3862944 0.0000000 0.0000000 0.0000000 0.0053899 0.0081547 0.0058824 0.0058824 0.0058824 0.0553806 0.0553806 882
hamlet thrift 1 2 0.0058824 1.3862944 0.0000000 0.0000000 0.0000000 0.0053899 0.0081547 0.0058824 0.0058824 0.0058824 0.0553806 0.0553806 882
hamlet make 2 3 0.0088235 0.6931472 0.0034364 0.0008591 0.0034364 0.0045073 0.0061160 0.0053871 0.0079644 0.0053871 0.0473864 0.0117273 878
hamlet sword 2 3 0.0088235 0.6931472 0.0034364 0.0008591 0.0034364 0.0045073 0.0061160 0.0053871 0.0079644 0.0053871 0.0473864 0.0117273 878

Extract the metrics for Horatio

get_doc_metrics(scores, "horatio") %>% 
  arrange(desc(REL_TF_NRTF)) %>% 
  head(10) %>% 
  kable()
Doc Word In Docs Count TF IRF RTF NRTF MRTF TF_IDF TF_IRF TF_RTF TF_NRTF TF_MRTF REL_TF_NRTF REL_TF_MRTF RANK_ENS
horatio lord 5 37 0.0831461 -0.2231436 0.1203392 0.0300848 0.1065292 -0.0151593 -0.0185535 0.0000000 0.0530613 0.0000000 0.1456518 0.0000000 629
horatio might 1 4 0.0089888 1.3862944 0.0000000 0.0000000 0.0000000 0.0082363 0.0124611 0.0089888 0.0089888 0.0089888 0.1208332 0.1208332 887
horatio heard 2 3 0.0067416 0.6931472 0.0018868 0.0004717 0.0018868 0.0034438 0.0046729 0.0048548 0.0062699 0.0048548 0.0370797 0.0128226 886
horatio aught 1 2 0.0044944 1.3862944 0.0000000 0.0000000 0.0000000 0.0041182 0.0062305 0.0044944 0.0044944 0.0044944 0.0302083 0.0302083 879
horatio bernardo 1 2 0.0044944 1.3862944 0.0000000 0.0000000 0.0000000 0.0041182 0.0062305 0.0044944 0.0044944 0.0044944 0.0302083 0.0302083 879
horatio consider 1 2 0.0044944 1.3862944 0.0000000 0.0000000 0.0000000 0.0041182 0.0062305 0.0044944 0.0044944 0.0044944 0.0302083 0.0302083 879
horatio custom 1 2 0.0044944 1.3862944 0.0000000 0.0000000 0.0000000 0.0041182 0.0062305 0.0044944 0.0044944 0.0044944 0.0302083 0.0302083 879
horatio een 1 2 0.0044944 1.3862944 0.0000000 0.0000000 0.0000000 0.0041182 0.0062305 0.0044944 0.0044944 0.0044944 0.0302083 0.0302083 879
horatio issue 1 2 0.0044944 1.3862944 0.0000000 0.0000000 0.0000000 0.0041182 0.0062305 0.0044944 0.0044944 0.0044944 0.0302083 0.0302083 879
horatio most 1 2 0.0044944 1.3862944 0.0000000 0.0000000 0.0000000 0.0041182 0.0062305 0.0044944 0.0044944 0.0044944 0.0302083 0.0302083 879

Extract the metrics for Ophelia

get_doc_metrics(scores, "ophelia") %>% 
  arrange(desc(REL_TF_NRTF)) %>% 
  head(10) %>% 
  kable()
Doc Word In Docs Count TF IRF RTF NRTF MRTF TF_IDF TF_IRF TF_RTF TF_NRTF TF_MRTF REL_TF_NRTF REL_TF_MRTF RANK_ENS
ophelia lord 5 31 0.1065292 -0.2231436 0.0969561 0.0242390 0.0831461 -0.0194226 -0.0237713 0.0095731 0.0822902 0.0233831 0.3571989 0.0309710 742
ophelia mark 1 3 0.0103093 1.3862944 0.0000000 0.0000000 0.0000000 0.0094463 0.0142917 0.0103093 0.0103093 0.0103093 0.1753109 0.1753109 887
ophelia know 4 6 0.0206186 0.0000000 0.0146223 0.0036556 0.0094340 0.0000000 0.0000000 0.0059962 0.0169630 0.0111846 0.0822374 0.0230834 762
ophelia better 1 2 0.0068729 1.3862944 0.0000000 0.0000000 0.0000000 0.0062975 0.0095278 0.0068729 0.0068729 0.0068729 0.0779159 0.0779159 883
ophelia keen 1 2 0.0068729 1.3862944 0.0000000 0.0000000 0.0000000 0.0062975 0.0095278 0.0068729 0.0068729 0.0068729 0.0779159 0.0779159 883
ophelia keep 1 2 0.0068729 1.3862944 0.0000000 0.0000000 0.0000000 0.0062975 0.0095278 0.0068729 0.0068729 0.0068729 0.0779159 0.0779159 883
ophelia many 1 2 0.0068729 1.3862944 0.0000000 0.0000000 0.0000000 0.0062975 0.0095278 0.0068729 0.0068729 0.0068729 0.0779159 0.0779159 883
ophelia naught 1 2 0.0068729 1.3862944 0.0000000 0.0000000 0.0000000 0.0062975 0.0095278 0.0068729 0.0068729 0.0068729 0.0779159 0.0779159 883
ophelia show 1 2 0.0068729 1.3862944 0.0000000 0.0000000 0.0000000 0.0062975 0.0095278 0.0068729 0.0068729 0.0068729 0.0779159 0.0779159 883
ophelia sings 1 2 0.0068729 1.3862944 0.0000000 0.0000000 0.0000000 0.0062975 0.0095278 0.0068729 0.0068729 0.0068729 0.0779159 0.0779159 883

Extract and stack metrics for all documents

stack_doc_metrics(scores)
#> # A tibble: 1,294 x 17
#>    Doc   Word  `In Docs` Count      TF    IRF     RTF    NRTF    MRTF   TF_IDF
#>    <chr> <chr>     <dbl> <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>    <dbl>
#>  1 clau… aboa…         1     1 0.00189  1.39  0       0.      0        1.73e-3
#>  2 clau… acqu…         1     1 0.00189  1.39  0       0.      0        1.73e-3
#>  3 clau… affe…         1     1 0.00189  1.39  0       0.      0        1.73e-3
#>  4 clau… alas          3     2 0.00377  0.288 0.0149  3.73e-3 0.0120   8.42e-4
#>  5 clau… alone         2     1 0.00189  0.693 0.00294 7.35e-4 0.00294  9.64e-4
#>  6 clau… and           5     7 0.0132  -0.223 0.0663  1.66e-2 0.0180  -2.41e-3
#>  7 clau… answ…         3     1 0.00189  0.288 0.00524 1.31e-3 0.00299  4.21e-4
#>  8 clau… apart         2     1 0.00189  0.693 0.00299 7.49e-4 0.00299  9.64e-4
#>  9 clau… argu…         2     1 0.00189  0.693 0.00344 8.59e-4 0.00344  9.64e-4
#> 10 clau… arm           2     1 0.00189  0.693 0.00344 8.59e-4 0.00344  9.64e-4
#> # … with 1,284 more rows, and 7 more variables: TF_IRF <dbl>, TF_RTF <dbl>,
#> #   TF_NRTF <dbl>, TF_MRTF <dbl>, REL_TF_NRTF <dbl>, REL_TF_MRTF <dbl>,
#> #   RANK_ENS <dbl>

Metrics

TF-IDF and TF-IRF (Term Frequency - Inverse Rest Frequency)

These are highly correlated (>0.999).

equation

equation

equation

equation

equation

TF-RTF (Term Frequency - Rest Term Frequency)

TF-RTF is positive when the term frequency is higher in the current document than the sum of the term frequencies in the rest of the corpus.

equation

equation

TF-NRTF (Term Frequency - Normalized Rest Term Frequency)

As our selected TF function ensures that frequencies add up to 1 document-wise, the NRTF (Normalized Rest Term Frequency) is simply the average term frequency in the other documents, instead of the sum as in RTF.

TF-NRTF is positive when the term frequency is higher in the current document than the average term frequency in the rest of the corpus.

equation

equation

TF-MRTF (Term Frequency - Maximum Rest Term Frequency)

Instead of the normalized/average rest term frequency, we instead use the maximum rest term frequency.

TF-MRTF is positive when the term frequency is higher in the current document than the maximum term frequency in the rest of the corpus.

equation

equation

Relative TF-NRTF (Relative Term Frequency - Normalized Rest Term Frequency)

Where the TF-NRTF tend to be dominated by highly frequent words, the Relative TF-NRTF instead uses the relative distance to the NRTF. As that would likely be dominated by very infrequent words, we multiply it by the term frequency.

equation

equation

Epsilon (ε) is added to avoid zero-division. It is calculated to resemble +1 smoothing in the rest population.

The beta (β) exponentiator allows us to control the influence of the term frequency. By setting it to 0, we simply get the relative difference (log scaled).

Relative TF-MRTF (Relative Term Frequency - Maximum Rest Term Frequency)

Similar to Relative TF-NRTF but for MRTF instead.

equation

vocabular2's People

Contributors

ludvigolsen avatar

Stargazers

Andrew Allen Bruce avatar rbkh avatar

Watchers

James Cloos avatar avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.