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<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>Dear Amanda, Justin, et al,<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>I agree that a “sentiment detection module”
hasn’t been perfected yet. The problem, as Amanda puts it, is that <o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>“</span></font>once you start
working with real data, both statistical and grammatical ('semantic')
approaches will fail. You need a more complex model of information in
order to be able to understand that a tweet such as "Bummer, I left my
iPhone on the bus - I'm lost without it :-(",<font size=2 color=blue
face=Arial><span style='font-size:10.0pt;font-family:Arial;color:blue'>”<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>The answer, IMHO, is to use large corpora
of said real data, and the best way to handle it is to first, have analysts
annotate the expressions for their own perceived sentiment. Second, use the
database partitioning techniques I have mentioned before to organize those
statement found to have the same sentiment rating into groups, third to
reorganize each group into subclasses, and finally to select key words and key
grammar fragments that match the observed patterns. If still not satisfied,
then fourth recurse on each subclass of statement. For any newcomers, my
recommendations are encapsulated at<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><a
href="http://www.englishlogickernel.com/Patent-7-209-923-B1.pdf">www.englishlogickernel.com/Patent-7-209-923-B1.pdf</a>
<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>But it will take LOTS of data to capture
even MOST of the comment styles. <o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>-Rich<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<div>
<p class=MsoNormal><font size=3 color=black face="Times New Roman"><span
style='font-size:12.0pt;color:black'>Sincerely,<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 color=black face="Times New Roman"><span
style='font-size:12.0pt;color:black'>Rich Cooper<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 color=black face="Times New Roman"><span
style='font-size:12.0pt;color:black'>EnglishLogicKernel.com</span></font><font
color=blue><span style='color:blue'><o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 color=black face="Times New Roman"><span
style='font-size:12.0pt;color:black'>Rich AT EnglishLogicKernel DOT com</span></font><font
color=blue><span style='color:blue'><o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 color=black face="Times New Roman"><span
style='font-size:12.0pt;color:black'>9 4 9 \ 5 2 5 - 5 7 1 2</span></font><o:p></o:p></p>
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<p class=MsoNormal><b><font size=2 face=Tahoma><span style='font-size:10.0pt;
font-family:Tahoma;font-weight:bold'>From:</span></font></b><font size=2
face=Tahoma><span style='font-size:10.0pt;font-family:Tahoma'>
corpora-bounces@uib.no [mailto:corpora-bounces@uib.no] <b><span
style='font-weight:bold'>On Behalf Of </span></b>Amanda Schiffrin<br>
<b><span style='font-weight:bold'>Sent:</span></b> Friday, December 16, 2011
12:21 PM<br>
<b><span style='font-weight:bold'>To:</span></b> Justin Washtell<br>
<b><span style='font-weight:bold'>Cc:</span></b> corpora@uib.no<br>
<b><span style='font-weight:bold'>Subject:</span></b> Re: [Corpora-List]
EmoText - Software for opinion mining and lexical affect sensing</span></font><o:p></o:p></p>
</div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>I think Justin has hit the nail on the head here. I worked on an
attempt to develop a sentiment detection module for a text analytics software
system in my previous job, and I soon realised that once you start working with
real data, both statistical and grammatical ('semantic') approaches will fail.
You need a more complex model of information in order to be able to
understand that a tweet such as "Bummer, I left my iPhone on the bus - I'm
lost without it :-(", despite containing only indicators of negative
sentiment at the lexical level, still expresses high positive sentiment toward
the *product*. Being able to distinguish this kind of sentiment is one of
the main drivers of commercial sentiment detection, and I'd say we're still a
very long way away from anything like that level of sophistication.<o:p></o:p></span></font></p>
<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><o:p> </o:p></span></font></p>
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<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>Mandy Schiffrin<o:p></o:p></span></font></p>
</div>
<div>
<div>
<p class=MsoNormal style='margin-bottom:12.0pt'><font size=3
face="Times New Roman"><span style='font-size:12.0pt'><o:p> </o:p></span></font></p>
<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>On 16 December 2011 20:24, Justin Washtell <<a
href="mailto:lec3jrw@leeds.ac.uk">lec3jrw@leeds.ac.uk</a>> wrote:<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>"I would be very sad if this movie did not win a prize."
high_neg<br>
"I'm very happy that the other reviewers have seen this movie for what it
is: rubbish." high_pos<br>
<br>
Rather than (unfairly) singling out this system, I think these examples serve
to highlight that this is a very difficult (if not impossibly ill-defined)
problem. One cannot just assess the polarity of a statement - one needs to know
something about what the object of interest is. In the above cases we are
probably interested in [the writer's opinion of] the movie... but that fact is
of course *pragmatic* information.<br>
<br>
I'm out of my depth now, so I'll say no more :-) No doubt much has been written
on these issues.<o:p></o:p></span></font></p>
<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><br>
Justin Washtell<br>
<st1:place w:st="on"><st1:PlaceType w:st="on">University</st1:PlaceType> of <st1:PlaceName
w:st="on">Leeds</st1:PlaceName></st1:place><br>
<br>
________________________________________<o:p></o:p></span></font></p>
</div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>From: <a href="mailto:corpora-bounces@uib.no">corpora-bounces@uib.no</a>
[<a href="mailto:corpora-bounces@uib.no">corpora-bounces@uib.no</a>] On Behalf
Of Angus Grieve-Smith [<a href="mailto:grvsmth@panix.com">grvsmth@panix.com</a>]<br>
Sent: 16 December 2011 17:25<br>
To: <a href="mailto:corpora@uib.no">corpora@uib.no</a><br>
Subject: Re: [Corpora-List] EmoText - Software for opinion mining and lexical
affect sensing<o:p></o:p></span></font></p>
<div>
<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><br>
On 12/16/2011 9:01 AM, Alexander Osherenko wrote:<br>
> You didn't test the approach for complex sentences. I always used the<br>
> example "I am very sad if ..."<br>
<br>
I don't want to nitpick, but that's not a very nativelike example<br>
for a test sentence. I've only heard English speakers use "I am very<br>
sad if ..." in habitual or generic contexts, and even then "I get
very<br>
sad when ..." is much more common. "I would be very sad if
..." is also<br>
used. Maybe check your test sentences against the CoCA or something?<br>
<br>
--<br>
-Angus B. Grieve-Smith<br>
<a href="mailto:grvsmth@panix.com">grvsmth@panix.com</a><br>
<br>
<br>
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