[Sigmetrics] Co-word Maps and Topic Modeling: A Comparison from a User's Perspective

Loet Leydesdorff loet at leydesdorff.net
Wed Nov 11 00:06:12 EST 2015

Co-word Maps and Topic Modeling: A Comparison from a User's Perspective

Induced by "big data," "topic modeling" has become an attractive alternative
to mapping co-words in terms of co-occurrences and co-absences using network
techniques. We return to the word/document matrix using first a single text
with a strong argument ("The Leiden Manifesto") and then upscale to a sample
of moderate size (n = 687) to study the pros and cons of the two approaches
in terms of the resulting possibilities for making semantic maps that can
serve an argument. The results from co-word mapping (using two different
routines) versus topic modeling are significantly uncorrelated. Whereas
components in the co-word maps can easily be designated, the coloring of the
nodes according to the results of the topic model provides maps that are
difficult to interpret. In these samples, the topic models seem to reveal
similarities other than semantic ones (e.g., linguistic ones). In other
words, topic modeling does not replace co-word mapping. 


Loet Leydesdorff and Adina Nerghes


** apologies for cross-postings



Loet Leydesdorff 

Professor, University of Amsterdam
Amsterdam School of Communication Research (ASCoR)

 <mailto:loet at leydesdorff.net> loet at leydesdorff.net ;
<http://www.leydesdorff.net/> http://www.leydesdorff.net/ 
Honorary Professor,  <http://www.sussex.ac.uk/spru/> SPRU, University of

Guest Professor  <http://www.zju.edu.cn/english/> Zhejiang Univ., Hangzhou;
Visiting Professor,  <http://www.istic.ac.cn/Eng/brief_en.html> ISTIC,

Visiting Professor,  <http://www.bbk.ac.uk/> Birkbeck, University of London;



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