Alison, I can't help with the "how-to" but I do like the way that the
Open Library displays subjects and related subjects broken into
"subject, places, people, times":
https://openlibrary.org/subjects/evolution
What looks helpful to me is the treatment of subdivisions by their
nature. (The correlation of related subjects is very interesting, but
that may not be what you are aiming at.) It does look to me that you may
need to use more than the 650 fields, as some topics seem to be covered
in both a 650 and another 6xx:
# 650_0 |*a* Estuaries |*z* Erie, Lake |*v* Congresses.
# 651_0 |*a* Erie, Lake |*v* Congresses.
(Don't get me started on the mess that LCSH is.)
kc
On 1/4/24 8:26 AM, Alison Clemens wrote:
> Hi, all,
>
> Has anyone here done text analysis-type work on MARC data, particularly on
> topical subject headings? I work closely with my library's digital
> collections, and I am interested in seeing what kinds of topics (as
> indicated in our descriptive data) are represented in our
> digital collections. So, I have the corresponding MARCXML for the
> materials and have extracted the 650s as a string (e.g., *650 $a World War,
> 1914-1918 $x Territorial questions $v Maps*), but I'm a little stuck on how
> to meaningfully analyze the data. I tried feeding the data into Voyant, but
> I think it's too large of a corpus to run properly there, and regardless,
> the MARC data is (of course) delimited in a specific way.
>
> Any / all perspectives or experience would be welcome -- please do get in
> touch directly ([log in to unmask]), if you'd like.
>
> With thanks,
> Alison Clemens
> Beinecke Rare Book and Manuscript Library, Yale University
--
Karen Coyle
[log in to unmask]
http://kcoyle.net
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