Would be more user-friendly if the articles were colour coded accordingly.
Very interesting too, I'd also love to know what kind or procedure you use
to detect the article type.
Phil Mossop
[EMAIL PROTECTED]
----- Original Message -----
From: "Matthew Hurst" <[EMAIL PROTECTED]>
To: <[email protected]>
Sent: Saturday, July 09, 2005 11:31 PM
Subject: Re: [backstage] Project Mood News - Alpha Stage
Davy,
I really liked your prototype. I've worked for a while on something called
sentiment detection, which is to do with determining whether an author
likes or dislikes some topic/product. What you are doing has a lot of
similarities,
although there is less emotional language in news - which is generally
meant to
be objective, though I think that more and more evaluative language is
slipping
in there.
You probably don't want to let on too much about how you are doing things,
but I would be interested to know a little more about the type of approach
you
are taking. Is it lexical/grammatical, or is it some form of machine
learning/statistical
classification based on some form of training?
I guess a naive approach would use phrases that indicated topics which
were generally good or bad. However, that approach would be easily
thwarted
by things like 'bombing suspect arrested' - which one might think of
as good news.
Anyway, I'd love to hear more about your approach.
Matt Hurst
Senior Research Scientist
Intelliseek, Inc
BlogPulse.com
On 7/8/05, Davy Mitchell <[EMAIL PROTECTED]> wrote:
Hi Folks,
Just signed up last night so HI!
I've just published an Alpha stage static page of my project Mood News
An overview of the days news from the BBC, auto-classified as Good,
Bad or Neutral.
http://www.latedecember.com/sites/moodnews/
Please provide feedback :-) Yeah the formating needs improved...
Thanks,
Davy Mitchell
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