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README.md

subtools

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Read, write and manipulate subtitles in R

Hi! Here, you will find some basic informations to get started with subtools. For more details, you can check the package documentation.

Subtools is a R package to read, write and manipulate subtitles in R. This then allows the full range of tools offered by the R ecosystem to be used for the analysis of subtitles. With version 1.0, subtools integrates the main principles of the tidyverse and integrates directly with tidytext for a tidy approach of subtitle text mining.

Install

To install the package from Github you can use devtools:

devtools::install_github("fkeck/subtools")
library(subtools)
library(tidytext)

Reading subtitles

The main goal of subtools is to provide a seamless way to import subtitle files directly into R. This task can be performed with the function read_subtitles():

rushmore_sub <- read_subtitles("ex_Rushmore.srt")
oss_sub <- read_subtitles("ex_OSS_117.srt")
rushmore_sub
#> # A tibble: 4 x 4
#>   ID    Timecode_in Timecode_out Text_content                              
#>   <chr> <time>      <time>       <chr>                                     
#> 1 180   20'40.969"  20'48.269"   Rushmore deserves an aquarium. A first cl…
#> 2 181   20'48.269"  20'50.870"   - I don't know. What do you think, Ernie …
#> 3 182   20'50.946"  20'57.370"   - What kind of fish? - Barracudas. Stingr…
#> 4 183   20'58.051"  21'01.770"   - Piranhas? Really? - Yes, I'm talking to…

oss_sub
#> # A tibble: 3 x 4
#>   ID    Timecode_in Timecode_out Text_content                              
#>   <chr> <time>      <time>       <chr>                                     
#> 1 264   20'22.967"  20'27.427"   Si vous voulez. Ça sera surtout l'occasio…
#> 2 265   20'30.347"  20'32.297"   Et non pas le gratin de pommes de terre.  
#> 3 266   20'35.587"  20'37.697"   Parce que ça ressemble à carotte, cairote.

The function read_subtitles() returns an object of class subtitles. This is a simple tibble with at least four columns (“ID”, “Timecode_in”, “Timecode_out” and “Text_content”).

The metadata are handled by adding extra-columns which can be used during the analysis. You can add metadata by adding columns manually (e.g. using mutate()). You can also provide a 1-row data.frame of metadata to the function read_subtitles().

bb_meta <- data.frame(Name = "Breaking Bad", Season = 1, Episode = 1)
bb_sub <- read_subtitles("ex_Breaking_Bad.srt", metadata = bb_meta)
bb_sub
#> # A tibble: 5 x 7
#>   ID    Timecode_in Timecode_out Text_content         Name   Season Episode
#>   <chr> <time>      <time>       <chr>                <fct>   <dbl>   <dbl>
#> 1 5     01'09.236"  01'12.780"   Oh, my God. Christ!  Break…      1       1
#> 2 6     01'15.993"  01'18.661"   Shit.                Break…      1       1
#> 3 7     01'18.829"  01'21.205"   [SIRENS WAILING IN … Break…      1       1
#> 4 8     01'24.918"  01'27.378"   Oh, God. Oh, my God. Break…      1       1
#> 5 9     01'27.546"  01'30.840"   Oh, my God. Oh, my … Break…      1       1
Series

If you want to analyze subtitles of series with different seasons and episodes, you will have to import many files at once. The read_subtitles_season(), read_subtitles_serie() and read_subtitles_multiseries() functions can make your life much easier, by making it possible to automatically import files and extract metadata from a structured directory. You can check the manual for more details.

MKV

Finally if you have a collection of movies in .mkv format, you can extract the subtitle tracks of MKV files with read_subtitles_mkv().

Cleaning subtitles

Often, the workflow begins with a cleaning step to get rid of irrelevant information that might be present in text content. Three functions can be used for this task. First, clean_tags() cleans formatting tags. By default, this function is automatically executed by the read_subtitles*() functions, so you probably don’t need to run it again. Second, clean_captions() can be used to supress closed captions, i.e. descriptions of non-speech elements in parentheses or squared brackets. Finally, clean_patterns() is a more general function to clean subtitles based on regex pattern matching.

bb_sub
#> # A tibble: 5 x 7
#>   ID    Timecode_in Timecode_out Text_content         Name   Season Episode
#>   <chr> <time>      <time>       <chr>                <fct>   <dbl>   <dbl>
#> 1 5     01'09.236"  01'12.780"   Oh, my God. Christ!  Break…      1       1
#> 2 6     01'15.993"  01'18.661"   Shit.                Break…      1       1
#> 3 7     01'18.829"  01'21.205"   [SIRENS WAILING IN … Break…      1       1
#> 4 8     01'24.918"  01'27.378"   Oh, God. Oh, my God. Break…      1       1
#> 5 9     01'27.546"  01'30.840"   Oh, my God. Oh, my … Break…      1       1

bb_sub_clean <- clean_captions(bb_sub)
bb_sub_clean
#> # A tibble: 4 x 7
#>   ID    Timecode_in Timecode_out Text_content         Name   Season Episode
#>   <chr> <time>      <time>       <chr>                <fct>   <dbl>   <dbl>
#> 1 5     01'09.236"  01'12.780"   Oh, my God. Christ!  Break…      1       1
#> 2 6     01'15.993"  01'18.661"   Shit.                Break…      1       1
#> 3 8     01'24.918"  01'27.378"   Oh, God. Oh, my God. Break…      1       1
#> 4 9     01'27.546"  01'30.840"   Oh, my God. Oh, my … Break…      1       1

Binding subtitles

Sometimes you will need to bind several subtitle objects together. This can be achieved with the function bind_subtitles(). This function is very similar to bind_rows from dplyr (they both bind rows of tibbles), but bind_subtitles() allows to recalculate timecodes to follow concatenation order (this can be disabled by setting sequential to FALSE).

bind_subtitles(rushmore_sub, oss_sub, bb_sub_clean)
#> # A tibble: 11 x 7
#>    ID    Timecode_in Timecode_out Text_content         Name  Season Episode
#>    <chr> <time>      <time>       <chr>                <fct>  <dbl>   <dbl>
#>  1 180   20'40.969"  20'48.269"   Rushmore deserves a… <NA>      NA      NA
#>  2 181   20'48.269"  20'50.870"   - I don't know. Wha… <NA>      NA      NA
#>  3 182   20'50.946"  20'57.370"   - What kind of fish… <NA>      NA      NA
#>  4 183   20'58.051"  21'01.770"   - Piranhas? Really?… <NA>      NA      NA
#>  5 264   41'24.737"  41'29.197"   Si vous voulez. Ça … <NA>      NA      NA
#>  6 265   41'32.117"  41'34.067"   Et non pas le grati… <NA>      NA      NA
#>  7 266   41'37.357"  41'39.467"   Parce que ça ressem… <NA>      NA      NA
#>  8 5     42'48.703"  42'52.247"   Oh, my God. Christ!  Brea…      1       1
#>  9 6     42'55.460"  42'58.128"   Shit.                Brea…      1       1
#> 10 8     43'04.385"  43'06.845"   Oh, God. Oh, my God. Brea…      1       1
#> 11 9     43'07.013"  43'10.307"   Oh, my God. Oh, my … Brea…      1       1

Some functions under certain conditions can also return a list of subtitle objects (class multisubtitles). The function bind_subtitles() can also be used on such object to bind each elements into a new subtitle object, i.e. something similar to do.call(rbind, x).

multi_sub <- bind_subtitles(rushmore_sub, bb_sub_clean, collapse = FALSE, sequential = FALSE)
multi_sub
#> A multisubtitles object with 2 elements
#> subtitles object [[1]]
#> # A tibble: 4 x 4
#>   ID    Timecode_in Timecode_out Text_content                              
#>   <chr> <time>      <time>       <chr>                                     
#> 1 180   20'40.969"  20'48.269"   Rushmore deserves an aquarium. A first cl…
#> 2 181   20'48.269"  20'50.870"   - I don't know. What do you think, Ernie …
#> 3 182   20'50.946"  20'57.370"   - What kind of fish? - Barracudas. Stingr…
#> 4 183   20'58.051"  21'01.770"   - Piranhas? Really? - Yes, I'm talking to…
#> 
#> 
#> subtitles object [[2]]
#> # A tibble: 4 x 7
#>   ID    Timecode_in Timecode_out Text_content         Name   Season Episode
#>   <chr> <time>      <time>       <chr>                <fct>   <dbl>   <dbl>
#> 1 5     01'09.236"  01'12.780"   Oh, my God. Christ!  Break…      1       1
#> 2 6     01'15.993"  01'18.661"   Shit.                Break…      1       1
#> 3 8     01'24.918"  01'27.378"   Oh, God. Oh, my God. Break…      1       1
#> 4 9     01'27.546"  01'30.840"   Oh, my God. Oh, my … Break…      1       1

bind_subtitles(multi_sub)
#> # A tibble: 8 x 7
#>   ID    Timecode_in Timecode_out Text_content          Name  Season Episode
#>   <chr> <time>      <time>       <chr>                 <fct>  <dbl>   <dbl>
#> 1 180   20'40.969"  20'48.269"   Rushmore deserves an… <NA>      NA      NA
#> 2 181   20'48.269"  20'50.870"   - I don't know. What… <NA>      NA      NA
#> 3 182   20'50.946"  20'57.370"   - What kind of fish?… <NA>      NA      NA
#> 4 183   20'58.051"  21'01.770"   - Piranhas? Really? … <NA>      NA      NA
#> 5 5     22'11.006"  22'14.550"   Oh, my God. Christ!   Brea…      1       1
#> 6 6     22'17.763"  22'20.431"   Shit.                 Brea…      1       1
#> 7 8     22'26.688"  22'29.148"   Oh, God. Oh, my God.  Brea…      1       1
#> 8 9     22'29.316"  22'32.610"   Oh, my God. Oh, my G… Brea…      1       1

Tidying subtitles

The tidy text format as defined by Julia Silge and David Robinson is a table with one-token-per-row, a token being a meaningful unit of text, such as a word or a sentence. The objects returned by read_subtitles*() are in some ways already tidy (each row being a subtitle block associated with a timecode). However, this unit is not always the most relevant for data analysis. To perform tokenization, the tidytext package provides the generic function unnest_tokens(). The package subtools adds a new method to unnest_tokens() to handle subtitles objects. The main difference with the data.frame method is the possibility to perform timecode remapping according to the tokenisation process.

rushmore_sub
#> # A tibble: 4 x 4
#>   ID    Timecode_in Timecode_out Text_content                              
#>   <chr> <time>      <time>       <chr>                                     
#> 1 180   20'40.969"  20'48.269"   Rushmore deserves an aquarium. A first cl…
#> 2 181   20'48.269"  20'50.870"   - I don't know. What do you think, Ernie …
#> 3 182   20'50.946"  20'57.370"   - What kind of fish? - Barracudas. Stingr…
#> 4 183   20'58.051"  21'01.770"   - Piranhas? Really? - Yes, I'm talking to…

unnest_tokens(rushmore_sub)
#> # A tibble: 49 x 4
#>    ID    Timecode_in Timecode_out Text_content
#>    <chr> <time>      <time>       <chr>       
#>  1 180   20'40.9700" 20'41.4858"  rushmore    
#>  2 180   20'41.4868" 20'42.0026"  deserves    
#>  3 180   20'42.0036" 20'42.1318"  an          
#>  4 180   20'42.1328" 20'42.6486"  aquarium    
#>  5 180   20'42.6496" 20'42.7132"  a           
#>  6 180   20'42.7142" 20'43.0363"  first       
#>  7 180   20'43.0373" 20'43.3593"  class       
#>  8 180   20'43.3603" 20'43.8761"  aquarium    
#>  9 180   20'43.8771" 20'44.1991"  where       
#> 10 180   20'44.2001" 20'44.8451"  scientists  
#> # … with 39 more rows

unnest_tokens(bb_sub_clean, token = "sentences")
#> # A tibble: 8 x 7
#>   ID    Timecode_in Timecode_out Text_content      Name      Season Episode
#>   <chr> <time>      <time>       <chr>             <fct>      <dbl>   <dbl>
#> 1 5     01'09.2370" 01'11.4018"  oh, my god.       Breaking…      1       1
#> 2 5     01'11.4028" 01'12.7800"  christ!           Breaking…      1       1
#> 3 6     01'15.9940" 01'18.6610"  shit.             Breaking…      1       1
#> 4 8     01'24.9190" 01'25.9538"  oh, god.          Breaking…      1       1
#> 5 8     01'25.9548" 01'27.3780"  oh, my god.       Breaking…      1       1
#> 6 9     01'27.5470" 01'28.4087"  oh, my god.       Breaking…      1       1
#> 7 9     01'28.4097" 01'29.2714"  oh, my god.       Breaking…      1       1
#> 8 9     01'29.2724" 01'30.8400"  think, think, th… Breaking…      1       1

Note that unlike the data.frame method, the input and output arguments are optional. This is because here the Text_content column can be assumed to be the column of interest.

Once your data are ready, you can analyze them. I recommend you to have a look at Text Mining with R: A Tidy Approach by Julia Silge and David Robinson. This is a great place to get started with text mining in R.

Applications

A list of cool projects using subtools.

Note that these project used the branch 0.x of subtools. The API is totally different in subtools 1.0.

You beautiful, naïve, sophisticated newborn series by ma_salmon

A tidy text analysis of Rick and Morty by tudosgar

Rick and Morty and Tidy Data Principles (part 1) (part 2) (part 3) by pachamaltese

Term Frequencies by Season by tdawry

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