Internet Article Comment Classifier

INTERNET
ARTICLE
C OMMENT
 C LASSIFIE
RMatt
Jones,
Eric
Ma,
Prasanna
Vasudevan 
Stanford
CS
229
 –
Professor
Andrew
Ng 
December
2008
 
1 


INTRODUCTION 

1.1 


BACKGROUND 

Part
of
the
Web
2.0
revolution
of
the
Internet
in
the
past
few
years
has
been
the
explos
ioofn
user
comments
on
articles,
blogs,
media,
and
other
uploaded
content
on
various
websites
(e.g.
Slashdot,
Digg).
Many
of
these
comments
are
positive,
facilitating
discussion
and
adding
humor
to
the
webpage;
however,
there
are
also
a
multitude
of
comments 
–
including
spam/advertisements,
blatantly
offensive
posts,
trolls,
and
boring
posts–

that
detract
from
the
ease
of
reading
of
a
page
and
contribute
nothing
to
the
discussion
around
it.

To
alleviate
this
issue,
websites
like
Slashdot
have
implemented
a
comment ‐rating
system
where
users
can
not
only
post
comments,
but
rate
other
users’
comments.
This
way,
a
user
can
look
at
a
comment’s
rating
and
quality
modifier
(funny,
insightful,
etc.)
and
immediately
guess
whether
it
will
be
worth
reading

.The
site
can
even
filter
out
comments
below
a
threshold
so
the
user
never
has
to
see
them
(as
Slashdot
does).
 
1.2


GOAL

Despite
the
power
of
crowdsourcing,
ideally
a
website
should
be
able
to
“know”
how
interesting
or
useless
a
comment
is
as
soon
as
it
is
posted, 
so
it
can
be
brought
to
users’
attention
(via
placement
at
the
top
of
the
comments
section)
if
it
is
interesting
or
it
can
be
hidden
otherwise.
 
So ...