[Air-L] emotion detection machines - round 2

[Charles M. Ess]

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