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This is a Python implementation of David Hoover’s Analyze Textual Divisions Spreadsheet. The spreadsheet is a suite of macros that takes text that has been “lightly marked-up” and creates a spreadsheet that categorizes each line in the text file. This is a quick way to create divisions in a text (book, chapter, section). More importantly, it allows for a light tagging scheme to define dialogue in the text, giving a final file that lets you filter only one character’s dialogue, for example.

Markup syntax

Prof. Hoover’s markup is:

<1>    text division level 1
<2>    text division level 2
#<3>    text division level 3
#<4>    text division level 4
#[ ]       Letter writer
#{ }      Letter addressee
/          new speaker (character)
\          reporting clause (“speech marker”)
#>         copy without processing
#^          special character follows

Every tag that is commented out with a # is currently unimplemented.

Dialogue, as noted above, is triggered by the /. All text between that and the first or " is understood to be the character’s name, and everything after the quote marker is understood to be dialogue. It currently does not support UK-style single-quoting, guillemets («»), German-style low-9-quoting („“), or Russian/Joyce-style quotation dashes. It strikes me that converting those on the fly to the pattern the system does understand can be done with a vim macro (see below).

Dialogue ends with a blank line. Hence a line like:

“Hello,” said Alice, “And good-bye!” Then she walked away.

would be marked up as:

/Alice H.“Hello,”

\said Alice,

/Alice H.“And good-bye!”

Then she walked away.

With two handy vim macros (see below), breaking this up becomes rather easy. In a speakers export, Alice H.’s dialogue would be concatenated into a file called aliceh.txt.

Noting reporting clauses is done with a backslash, which lets them be separated from the regular narrative, if you like.

The file sample.txt is the file used for testing and also reveals how the markup can look in the wild.


For now, the usage is simply:


or you can install it with pip and then simply use: FILENAME [OUTPUT FILENAME]

Of course, --help

will include other tricks available from the command-line interface.

or you can get a bit fancier and make use of more methods using it as a module:

>>> import text_divider as td
>>> text = td.Text('sample.txt')
>>> speakers = text.all_speakers() # returns speakers sorted by lines of dialogue
>>> speakers
[(None, 18), ('Mr. Carraway', 3), ('Nick', 3), ('Daisy', 2), ('Tom', 2)]
>>> nick = text.speakers('Nick') # returns a string of all of the speaker’s dialogue
>>> nick
'The whole town is desolate. All the cars have the left rear wheel painted black as a mourning wreath, and there’s a persistent wail all night along the north shore.'
>>> text.export_top_speakers_to_txt(3, 'speakers_dir') # creates a “speakers_dir” and
# a separate text file for the top 3 speakers (including “none” for the narration)
# and collapses all the rest of the dialogue into a “minorspeakers.txt” file.


Using it with the command line gives a result similar to that of Prof. Hoover’s original spreadsheet. It creates a tab-delimited .csv file where each line of text is marked (or not) depending on what division it is in. For example, there might be a “SPEAKER” column, and the value for the lines could be blank, “Mr. Carraway,” or “Nick,” depending.

You can additionally pass the --speakers-export option with a path to a directory, into which the program will place a separate .txt file for each speaker. The same happens with --sections-export, but for sections.

Using it in the interpreter or within a program creates a list where each value is a dictionary with keys as the column names. The current column names are:

text: the line of text
section: the names of the sections as a tree (for example, “Book I - Chapter 1”)
speaker: the speaker of the current line, if one is named, or other marking.


I couldn’t get Prof. Hoover’s spreadsheet to work on my computer, but I also think that this tool makes it easy to feed customized text into NLTK. Specifically, I wanted a way to mark up a .txt version of The Great Gastby so that I could programmatically get the dialogue on the fly. It strikes me that the light markup of this program is more flexible and quicker to implement than, say, creating a TEI version of the novel, etc., etc.

vim macros

Given a raw set of lines like:

"Whenever you feel like criticizing any one," he told me, "just remember that
all the people in this world haven't had the advantages that you've had."

I use two macros to move through this quickly. First, on the top line, I use:


This gets us to:

/"Whenever you feel like criticizing any one," 

\he told me, "just remember that
all the people in this world haven't had the advantages that you've had."

with the cursor right after the /, meaning that one can quickly type (or paste) in the name of the speaker (Mr. Carraway in this case). Then you move to the next line and fire the next macro:


This gets us to:

/Mr. Carraway"Whenever you feel like criticizing any one," 

\he told me, 

/"just remember that all the people in this world haven't had the advantages that you've had."

With the cursor right by the second /, allowing you to type/paste in the name again. The macros can be changed to use or instead of ". The script recognizes both dialogue delimiters. For other quotation schemes, a quick substitution would be necessary. In the case of guillemets, for example:


Copyright, etc.

This program is © 2016, Moacir P. de Sá Pereira. It is available under the GNU GPL v. 3. See the LICENSE file for details.


A Python implementation of David Hoover's Analyze Textual Divisions Spreadsheet




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