[Air-L] emotion detection machines - round 2

[Annette Markham]

I agree that the issue is likely larger than simply the accuracy of Senpy. 

Perhaps you already discussed this, but what is the research question in this case? Is it necessary to analyze the comments and if so, why is a total dataset analysis required? If we can better understand the goal of the analysis, we can better assess potential ways to accomplish it. In the previous discussion, the research design seemed rather method or even tool driven, which might not be the optimal way to frame the design, since the tool is flawed. 

FWIW, I'd be happy to have this larger discussion,

annette


On 10/25/19, 16:23, "Air-L on behalf of Necip Enes GENGEÇ" <air-l-bounces at listserv.aoir.org on behalf of necipenesgengec at gmail.com> wrote:

    Hi Charles,
    
    I think it's a complete research problem by itself that you are struggling
    with. I didn't used Senpy before but it's a predictor at the end of the day
    and it'll only be close to real conclusion. Incase you want to reduce the
    manual work, I suggest running the analysis with multiple solutions eg.
    with Meltwater, Senpy etc. and drive a conclusion for the manual check from
    a combined result.
    
    Cheers,
    Necip
    
    25 Eki 2019 Cum 16:14 tarihinde Charles M. Ess <c.m.ess at media.uio.no> şunu
    yazdı:
    
    > Dear AoIRists,
    >
    > First of all, many thanks again to everyone who contributed suggestions
    > and critical comments regarding emotion detection machines.
    >
    > My student settled on Senpy, despite important criticisms and
    > limitations, and is now in a second phase of analysis.
    >
    > The student is collecting emotional analyses of both primary posts and
    > comments.  Primary posts range around 800 - comment posts are ca. 7000.
    > The student went through the primary posts, and found that Senpy misread
    > a post ca. 20% of the time, e.g., Senpy's analysis of a post might be
    > "sadness," but when read in context, the emotional response was clearly
    > happiness.
    >
    > Query: what is there to do, if anything, with Senpy's analysis of the
    > 7000+ comment posts?
    > It is not possible for the student to do the same process of manually
    > cleaning the data.
    > So: does the student just take up the results as they are, assuming that
    > there will likely be a 20% error rate and simply accept that as a limit
    > to the method / analysis?
    > Or: ???
    > I can't think of a good way forward here (no surprise: my PhD was on
    > Kant ...) - so I'm hoping very likely many AoIRists will have one or
    > more good solutions or suggestions.
    >
    > Many thanks in advance, and all best,
    > - charles
    >
    >
    > --
    > 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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