1. State
of
the
Union:
Social
Media
Report
This
report
is
an
analysis
of
the
Internet
conversations
relating
to
the
State
of
the
Union
address.
We
analyzed
many
millions
of
messages
from
sources
such
as
Twitter,
Blogs,
Social
Networks,
news
sources
and
other
online
publications
to
provide
true
measurement
and
understanding
of
messages
that
are
relevant
to
this
study.
Our
analysis
covers
messages
and
articles
around
the
time
of
the
televised
address,
before
and
after
the
event
to
provide
a
detailed
look
at
this
chatter.
Below
we
have
a
Trend
chart
that
shows
all
the
conversations
around
the
State
of
the
Union
address
and
what
percentage
of
that
conversation
was
dedicated
to
each
topic.
The
Y
axis
is
labeled
with
percentages.
This
is
our
normalized
Post
Reach:
This
value
is
the
total
number
of
post
results
matching
our
query
divided
by
the
total
number
of
results
for
the
State
of
the
Union.
Here
we
see
that
economy,
spending
and
healthcare
were
the
top
three
topics
being
talked
about
before
the
State
of
the
Union.
This
could
mean
that
people
were
expecting
these
to
be
the
most
talked
about
topics
in
the
address.
The
days
following
the
speech
we
see
a
very
large
percentage
of
the
conversation
being
focused
on
the
economy
and
spending.
It's
also
interesting
to
note
that
healthcare
conversations
continued
to
fall
in
relation
to
other
State
of
the
Union
chatter.

2. Sentiment
Analysis
for
Jobs
Analytics
is
a
powerful
tool
which
uses
automated
textual
analysis
(frequently
called
Natural
Language
Processing,
or
NLP)
to
determine
subject-­‐speciRic
sentiment
information,
topics
of
conversation
and
interesting
words
in
thousands
of
pieces
of
content
on
request
in
under
a
minute.
The
system
backing
Analytics
is
the
most
powerful
analysis
system
in
the
industry.
We
can
Rilter
results
based
on
the
queries
we
build.
This
means
that
we
can
actually
see
what
people
are
saying
speciRically
about
a
certain
topic.
In
this
instance
we
are
looking
at
the
sentiment
around
President
Obama
in
relation
to
jobs
and
unemployment.
Topic
Word
Cloud
The
Rirst
box
we
see
is
a
“Topic
Word
Cloud”.
This
box
contains
hot
topics
of
conversation
within
the
articles
around
jobs.
By
default,
the
words
are
sized
based
on
how
important
the
system
believed
them
to
be
in
these
conversations
(larger
being
more
signiRicant)
and
colored
based
on
sentiment
/
tone
averages
used
with
that
topic.
If
a
topic
is
green,
it
is
generally
referred
to
positively
in
this
context.
If
it
is
red,
it
is
frequently
negative.

3. Overall
Sentiment
Below,
we
see
two
pie
charts.
These
charts
show
the
overall
sentimental
tone
for
job-­‐related
conversations
in
relation
to
the
President.
The
left
side,
labeled
Sentiment
by
Subject
References,
shows
the
percentage
of
speciRic
references
to
jobs
which
were
positive,
negative
or
mixed
(mixed
being
those
which
were
both
positive
and
negative).
The
right
side,
labeled
Sentiment
by
Subject
Posts,
are
the
percentage
of
articles
or
posts
which
contained
sentiment
about
jobs
that
were
positive,
negative
or
mixed.
Sentiment
Trend
The
next
tool
we
see
is
our
sentiment
trend
which
shows
the
sentimental
tone
over
time.
We
can
see
some
big
spreads
early
in
September,
late
in
November,
and
again
in
late
December.

4. Word
&
Category
Analysis
Finally,
the
“Word
and
Category
Analysis”
shows
the
most
commonly
used
adjectives.
This
will
tell
us
what
percentage
of
the
posts
contained
these
adjectives
and
the
sentiment
behind
them.
We
also
see
in
the
last
column
a
list
of
categories
that
adjectives
fall
into.
This
is
helpful
to
see
the
context
of
the
sentimental
tone
and
how
much
of
this
content
falls
inside
these
categories.
Sentiment
Analysis
for
Spending
Here
we
analyze
sentiment
around
government
spending.
We
can
see
in
the
word
cloud
below
that
Democrats
are
viewed
more
negatively
than
Republicans
when
it
comes
to
spending.
Also,
we
see
that
Bush
is
mentioned
as
well.
In
this
case
people
are
defending
President
Obama
by
reminding
others
that
President
Bush
had
large
budget
deRicits
and
over
spent
signiRicantly.
Below
analytics,
we
can
see
posts
that
illustrate
this
point.
The
sentiment
trend
has
not
changed
much
over
time
but
we
do
see
some
signiRicant
spreads
throughout
the
last
few
months.

6. Post
/
Article
Viewer
Here
we
have
some
examples
of
the
posts
that
we
have
aggregated
into
our
database.
We
index
posts
as
they
appear
online.
The
posts
below
can
explain
some
of
the
sentiment
above.
Inside
the
tool
itself
we
are
able
to
click
the
blue
arrows
to
the
left
of
the
post
and
see
all
of
the
content.

7. Online
InRluence
Map
Top
Sources
is
a
powerful
tool
which
is
rather
unique.
The
data
generated
by
this
process
can
be
viewed
either
as
a
list
or
in
an
interactive
visualization
map.
In
this
instance
we
Rind
the
most
inRluential
websites
talking
about
President
Obama
over
the
last
year
and
a
half.
This
can
be
beneRicial
if
you
wanted
to
get
an
inRluential
third
party
blog
to
write
a
favorable
story
about
an
issue.
The
ecosystem
below
contains
the
top
100
online
sources
using
Social
Radar's
Top
Sources
Algorithm.
Top
sources
are
determined
both
by
the
amount
of
inRluence
of
the
source
and
other
factors
such
as
the
amount
of
relevant
posts.
Each
circle
represents
a
source,
and
each
line
represents
a
link.
You
can
bind
the
size
and
color
of
the
circles
to
different
attributes
to
help
you
in
determining
inRluence
and
activity.