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.<div>
<br></div><div>Mandy Schiffrin</div><div><div><br><br><div class="gmail_quote">On 16 December 2011 20:24, Justin Washtell <span dir="ltr"><<a href="mailto:lec3jrw@leeds.ac.uk">lec3jrw@leeds.ac.uk</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">"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.<br>
<div class="im"><br>
Justin Washtell<br>
University of Leeds<br>
<br>
________________________________________<br>
</div>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<br>
<div class="HOEnZb"><div class="h5"><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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