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Perhaps this vid might be of some relevant interest:
Contradicting Facebook: A Discourse Analysis. By Charlene Shovic
On Tue, Nov 6, 2012 at 9:25 AM, Moses Boudourides
<[log in to unmask]> wrote:
> Another option would be to use the python package nltk. Examples are
> in two books by Matt Russell (Mining the Social Wen and 21 Recipes for
> Mining Twitter, both O'Reilly). --Moses
> On Tue, Nov 6, 2012 at 12:31 AM, Derek Hansen <[log in to unmask]> wrote:
>> ***** To join INSNA, visit http://www.insna.org ***** Hi all,
>> I'm giving a guest lecture in a couple days on the intersection of Social
>> Network Analysis and Natural Language Processing, particularly as applied
>> toward the analysis of social media datasets. I know there's been a lot of
>> activity lately at the intersection of SNA and NLP and I'd love to see a few
>> pointers to some good quality work in this area.
>> Additionally, I'm hoping to flesh out a few high-level approaches for
>> merging the two in meaningful ways. Here are a few examples:
>> 1) Use content-based factors (extracted via NLP techniques) as well as
>> SNA-based metrics as independent variables that predict an outcome of
>> interest (e.g., quality of content, such as was done in Agichtein, Castillo,
>> et al. 2008).
>> 2) Use NLP to help define the edges in a network (e.g., "link polarity" as
>> performed by Kale 2007).
>> 3) Use a 2-step filtering process:
>> 3a) Use SNA to identify network clusters and then use NLP on the corpus
>> created by those within each cluster (e.g., Marc Smith's graphs on the
>> NodeXL Graph Gallery where keywords are overlaid on the network clusters)
>> 3b) Use NLP to identify subsets of "relevant" content whose authors are then
>> analyzed via SNA.
>> 4) SNA helps in disambiguating words (e.g., when one network cluster uses
>> the term "jaguar" they typically mean the sports team, while another network
>> cluster typically means the car).
>> Other thoughts on high-level strategies would be welcome as well.
>> Derek Hansen
>> Brigham Young University
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