[Air-L] emotion detection machine?

[Stuart Shulman]

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On Thu, Sep 5, 2019 at 7:25 AM Jill Walker Rettberg <
Jill.Walker.Rettberg at 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 at listserv.aoir.org på
> vegne av c.m.ess at 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 at media.uio.no
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