[Air-L] emotion detection machine?

Thu Sep 5 14:07:42 PDT 2019

Dear Charles,

Sally raises some excellent questions about the accuracy of sentiment analysis.  It is a blunt tool.

The categories used are likely drawn from Plutchik’s classic ‘Wheel of Emotions’:

Plutchik, Robert. 2003. Emotions and Life: Perspectives from Psychology, Biology, and Evolution. Washington, DC: American Psychological Association

Plutchik, Robert and Henry Kellerman eds. 1980. Emotion: Theory, research, and experience, Vol. 1. Theories of Emotion. New York: Academic Press.

Kind regards,
Fiona

DR FIONA MARTIN 
Senior Lecturer in Convergent and Online Media
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    Message: 12
    Date: Thu, 5 Sep 2019 13:11:18 -0700
    From: "Dr. S.A. Applin" <sally at sally.com>
    To: "Charles M. Ess" <c.m.ess at media.uio.no>
    Cc: air-l <air-l at listserv.aoir.org>
    Subject: Re: [Air-L] emotion detection machine?
    Message-ID: <FB3066F7-6431-43FB-90F1-A34C24307C59 at sally.com>
    Content-Type: text/plain;	charset=utf-8

    Dear Charles (and List),

    I see this as an ethics issue.

    How reliable are ?emotion analysis? tools? How would outcomes from them be used? 

    As you say, there is a lack of clarity in some in terms of ?explaining emotional categories.?  To me, this signals (along with obvious knowledge about the limitations and problems with algorithms), that there is opportunity here to be very, very, very wrong about people?s opinions, and any algorithmically interpreted ?emotional? state.

    For example, how would one interpret or finesse ?frustration,? vs ?anger??  The written word is contained within a language. Not all commenters will be native speakers to that language, and not all native speakers have the language tools required (even within their own language) to adequately express themselves, even in the best of times. What makes anyone think an algorithm would do better at this than a human trained in qualitative methods and with cultural and media and language knowledge? 

    There is way too much margin of potential error here for this to be automated, or  ?useful.? It is much more likely that things will be assumed incorrectly by limited algorithms in the first place.

    Furthermore, does your student see any problem with this exercise? That their tool analysis might get it very wrong? That the wrong might lead to assumptions or outcomes that are harmful to entities, people, governments? 

    What safeguards are in place for wrong assumptions and outcomes? 

    Kind regards,

    Sally 

    Sally Applin, Ph.D.
    ..........
    Research Fellow
    HRAF Advanced Research Centres (EU), Canterbury
    Centre for Social Anthropology and Computing (CSAC)
    ..........
    Research Associate
    Human Relations Area Files (HRAF)
    Yale University
    ..........
    Associate Editor, IEEE Consumer Electronics Magazine
    Member, IoT Council
    Executive Board Member: The Edward H. and Rosamond B. Spicer Foundation
    ..........
    https://protect-au.mimecast.com/s/JR-OCr8DLRtqVW9vtgnIZ0?domain=posr.org
    https://protect-au.mimecast.com/s/5UVtCvl0PoC9NRX6fLIk4l?domain=sally.com
    I am based in Silicon Valley
    ..........
    sally at sally.com | 650.339.5236

    > On Sep 5, 2019, at 3:52 AM, Charles M. Ess <c.m.ess at media.uio.no> wrote:
    > 
    > 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 (https://protect-au.mimecast.com/s/w8BUCXLKZoiQ5WJYtDwmjf?domain=senpy.gsi.upm.es) and  Twinword <https://protect-au.mimecast.com/s/BcVhCYWL1vi71w8xiV08Cd?domain=twinword.com> - 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
    > <https://protect-au.mimecast.com/s/k0fhCZYM2VF640GNFx8ASL?domain=hf.uio.no>
    > 
    > Postboks 1093
    > Blindern 0317
    > Oslo, Norway
    > c.m.ess at media.uio.no
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