[Air-L] emotion detection machine?
ali hürriyetoglu
ali.hurriyetoglu at gmail.com
Sun Sep 29 08:51:35 PDT 2019
Hi Aristea,
The following may help:
- Leidner, J. L., & Plachouras, V. (2017, April). Ethical by design: ethics
best practices for natural language processing. In *Proceedings of the
First ACL Workshop on Ethics in Natural Language Processing* (pp. 30-40).
- Hovy, D., & Spruit, S. L. (2016, August). The social impact of natural
language processing. In *Proceedings of the 54th Annual Meeting of the
Association for Computational Linguistics (Volume 2: Short Papers)* (pp.
591-598).
Best,
Ali
Aristea Fotopoulou <A.Fotopoulou at brighton.ac.uk>, 27 Eyl 2019 Cum, 14:43
tarihinde şunu yazdı:
> Dear all,
>
> Just revisiting this thread, and noticed some folk raised ethical issues
> in relation to sentiment analysis and emotional AI. I am looking for
> literature on the ethics of research using sentiment analysis tools and
> social media data (but also other types of data) and can¹t find much out
> there - have you come across anything or, better even, are you working on
> these issues?
>
> Andrew, I had a look at your website and approach - the critical issues
> you raise are interesting (summed up as 'is this OK?¹) and I¹d like to
> know more about how your team answers these questions but I can¹t find any
> answers. Are there any papers on methodologies and ethics at all coming
> out of the project?
>
> Many thanks,
> Aristea
>
>
>
> -----------------------------
>
> Dr Aristea Fotopoulou
> UKRI-AHRC Innovation Leadership Fellow
> PI ART/DATA/HEALTH: Data as creative material for Health & Wellbeing
> 2019-2021
> University of Brighton, School of Media
> Watts Building, Lewes Road, Brighton BN2 4GJ
>
> A.Fotopoulou at brighton.ac.uk |@aristeaf | https://aristeafotopoulou.org
> ART/DATA/HEALTH Research project: http://artdatahealth.org
> <http://artsdatahealth.org>
>
>
>
>
>
>
>
> On 12/09/2019 11:01, "Air-L on behalf of Charles M. Ess"
> <air-l-bounces at listserv.aoir.org on behalf of c.m.ess at media.uio.no> wrote:
>
> >Dear all -
> >a belated but most sincere thanks for all of this!
> >
> >I'll bundle up the thread, forward it to my student, and see what sense
> >we might be able to make of it all.
> >
> >Again, many thanks indeed and all best,
> >- charles
> >
> >On 07/09/2019 13:53, Shulman, Stu wrote:
> >> Sally,
> >>
> >> Machine generated sentiment analysis scores are sometimes abused as a
> >> shortcut to avoid certain forms of manual/mental labor in a variety of
> >> commercial and academic contexts. Language tools are in this scenario
> >> treated as a magic buttons to be deployed against corpora in the name
> >>of
> >> charts untouched by serious validation. I prefer it when humans are
> >> in-the-loop, which itself is recursive (meaning you repeat until there
> >> is no room to improve), using tools as filters to generate purposive
> >> samples that humans annotate and collectively validate using a
> >> systematic process.
> >>
> >> Sentiment problems range from hard to harder and hardest, where hardest
> >> means you cannot do it in a manner that can be validated by any means.
> >> There is no easy on this scale of tasks if false positive or negatives
> >> could cost a life or some other serious consequence, but to make it
> >> easier, requires a process, grossly boiled down below:
> >>
> >> 1. Collect a relevant and representative corpus of data,
> >> 2. Build a SPAM detection classifier to remove non-relevant data (ex.,
> >> wrong language OR no discernible sentiment),
> >> 3. Build a topic classifier and focus on one key topic first (not all
> >> topics at once),
> >> 4. Solve the Rubik's cube of how many codes and what they really mean
> >> (ex., happy/sad OR angry/frustrated/both/neither...),
> >> 5. Test the topic-specific annotation scheme with a group of no less
> >> than five independent annotators (not just two),
> >> 6. Crowd source the task to larger groups when possible, using memo
> >> writing to identify boundary cases that kill/modify models,
> >> 7. Use iteration to identify elite annotators through recursive
> >> validation, memo reviews, and scoring against a gold standard.
> >>
> >> The goal is to build task- and language-specific machine classifiers
> >> using the best possible human experts in the process. The main idea,
> >> however, is to keep a critical role for humans.
> >>
> >> ~Stu
> >>
> >>
> >> On Thu, Sep 5, 2019 at 4:11 PM Dr. S.A. Applin <sally at sally.com
> >> <mailto:sally at sally.com>> wrote:
> >>
> >> Dear Charles (and List),
> >>
> >> I see this as an ethics issue.
> >>
> >> How reliable are ³emotion analysis² tools? How would outcomes from
> >> them be used?
> >>
> >> As you say, there is a lack of clarity in some in terms of
> >> ³explaining emotional categories.² 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 people¹s opinions, and any algorithmically interpreted
> >> ³emotional² state.
> >>
> >> For example, how would one interpret or finesse ³frustration,² vs
> >> ³anger²? 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 ³useful.² 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
> >> ..........
> >> http://www.posr.org
> >> http://www.sally.com
> >> I am based in Silicon Valley
> >> ..........
> >> sally at sally.com <mailto: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
> >> <mailto: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 (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 <mailto:c.m.ess at media.uio.no>
> >> > _______________________________________________
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> >>
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> >>
> >>
> >> --
> >> Dr. Stuart W. Shulman
> >> Founder and CEO, Texifter
> >> Cell: 413-992-8513
> >> LinkedIn: http://www.linkedin.com/in/stuartwshulman
> >>
> >
> >--
> >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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