Dear Charles, There are many social listening tools on the corporate level: crimson hexagon and infegy which are media industry research methods for determining sentiment. What’s most important is making sure that you have a keyword set that is accurately parsing through the social media posts for sentiment. What’s always difficult is having a lexicon for local terms and also have a method for determining ambiguous posts where the person may mean something different then the keyword that is recognized. Best Wiebke Wiebke Reile, Ph.D. University of Hawai'i at Mānoa Communication and Information Sciences Adjunct Lecturer Brooklyn College Department of Television and Radio Wiebke.Reile@brooklyn.cuny.edu Online Adjunct Grand Canyon University College of Fine Arts and Production Wiebke.Reile@my.gcu.edu
On Sep 5, 2019, at 7:47 AM, Stuart Shulman <stuart.shulman@gmail.com> wrote:
Some tools for this work are born in academic research labs instead of corporate meeting rooms. For more than a decade we have offered free and commercial web-based tools for content labeling and annotator measurement in an academic research setting. To date there are 400+ interdisciplinary and multilingual citations:
https://discovertext.com/2018/03/31/scholarly-citations-of-the-coding-analys...
https://discovertext.com/publications/
Free demos: https://calendly.com/discovertext
~Stu
*Stu Shulman* <https://twitter.com/StuartWShulman>U.S. Soccer Federation C-Licensed Coach Valeo FC & Capacidad <http://capacidadprograms.org/?page_id=13> Volunteer Coach *Is your player ready to give back to the game? *Contact Coach Stu about fall 2019 volunteer efforts.
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On Thu, Sep 5, 2019 at 7:25 AM Jill Walker Rettberg < Jill.Walker.Rettberg@uib.no> wrote:
Dear Charles,
I'm not an expert, but I think she should be talking with linguists - and I think that what she's looking for is typically called sentiment analysis, not emotion analysis. There are probably tools for social media marketing that might be more readily accessible, but probably less scientifically transparent. https://en.wikipedia.org/wiki/Sentiment_analysis
Jill
Air-L på vegne av Charles M. Ess <air-l-bounces@listserv.aoir.org på vegne av c.m.ess@media.uio.no> skrev følgende den 05.09.2019, 12:53:
Dear colleagues,
One of our students is wanting to analyze emotional content in in the comment fields of a major newspaper vis-a-vis specific hot-button issues.
She has a good tool (I think) for scrapping the data - but she is stymied over the choice of an emotion analysis tool. She has looked at Senpy (http://senpy.gsi.upm.es/#test) and Twinword <https://www.twinword.com/api/emotion-analysis.php> - the latter seems the most accurate, but it is also expensive. She has recently discovered DepecheMood emotion lexicons (Staiano, J., & Guerini, M. (2014). Depechemood: a lexicon for emotion analysis from crowd-annotated news. arXiv preprint arXiv:1405.1605.) - but this suffers from a lack of clarity in terms of explaining its emotional categories: awe, indifference, sad, amusement , annoyance, joy, fear and anger.
For my part, I am entirely clueless. Any suggestions that she might pursue would be greatly appreciated.
best, - charles ess -- Professor in Media Studies Department of Media and Communication University of Oslo <http://www.hf.uio.no/imk/english/people/aca/charlees/index.html>
Postboks 1093 Blindern 0317 Oslo, Norway c.m.ess@media.uio.no _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
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