Presentation for the DC Social Data & Analytics Meetup, March 11, 2015

1. Social
Sentiment
Analysis
in
Social
Data
Seth
Grimes
Alta
Plana
Corporation
@sethgrimes
Social
Data
and
Analytics
–
DC
March
11,
2015

2. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
2
http://altaplana.com/TA2014

3. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
3
News
in
the
last
week…

4. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
4

5. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
5
technewstoday.com

6. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
6

7. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
7
Social
Data?
• Profiles.
• Identity,
categories.
• Connections.
• Elements:
Directionality,
degree.
• Content:
Text,
photos,
videos,
• Actions:
Likes,
Clicks,
Shares…
• Elements:
Time,
location,
target,
sequence.
Sentiment
can
be
extracted
or
inferred
from
all
of
these.
How?

8. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
8
Analytics
is
the
systematic
application
of
algorithmic
methods
that
derive
and
deliver
information,
typically
expressed
quantitatively,
whether
in
the
form
of
indicators,
tables,
visualizations,
or
models.
• Systematic
means
formal
&
repeatable.
• Algorithmic
contrasts
with
heuristic.
Analytics
creates
and/or
applies
models.

9. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
9
Sentiment
–
• Opinion,
attitude,
emotion,
and
mood:
Affective
states.
• Communicated
via
expressions
and
actions.
• Understood
contextually.
• Applied
situationally.
Anyone
who
tells
you
that
sentiment
analysis
is
solely
about
computing
a
positive/
negative/neutral(/mixed)
evaluation
is
probably
hawking
a
weak
solution.

10. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
10
The
sentiment
value
of
an
opinion
may
be
expressed
as
a
quintuple
(oj,
fjk,
soijkl,
hi,
tl):
• oj
is
a
target
object.
• fjk
is
an
feature
of
the
object
oj.
• hi
is
an
opinion
holder.
• tl
is
the
time
when
the
opinion
is
expressed.
• soijkl
is
the
sentiment
value
of
the
opinion
of
the
opinion
holder
hi
regarding
feature
fjk
of
object
oj
at
time
tl.
• soijkl
is
+ve,
-­‐ve,
or
neu,
or
a
more
granular
rating.
Bing
Liu,
NLP
Handbook

11. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
11
Comparative
opinions
-­‐-­‐
(O1,
O2,
F,
po,
h,
t):
• O1
and
O2
are
object
sets
being
compared
based
on
shared
features
F.
• po
is
the
preferred
object
set
of
the
opinion
holder
h.
• t
is
the
time
when
the
comparative
opinion
is
expressed.
Bing
Liu

12. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
12

13. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
13

14. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
14

15. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
15
Questions
for
business
(&
government):
What
are
people
saying?
What’s
hot/trending?
What
are
they
saying
about
{topic|person|product}
X?
...
about
X
versus
{topic|person|product}
Y?
How
has
opinion
about
X
and
Y
evolved?
How
has
opinion
correlated
with
{our|competitors’|
general}
{news|marketing|sales|events}?
Who
(and
What,
When
&
How)
are
opinion
leaders?
How
does
sentiment
propagate
across
multiple
channels?
What’s
behind
opinion,
the
root
causes?
(How)
Can
we
link
opinions,
profiles,
behaviors
&
transactions
to
discern
intent
and
predict
actions?

16. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
16
Measurement
methods
–
• Polling,
surveys
including
NPS.
• Natural
language
processing.
• Action
stats.
• Network
and
information-­‐flow
analysis.
• Biometrics.
Modelled
concepts
–
• Affinities
(clusters).
• Influence,
Authority.
• Satisfaction,
Loyalty,
Motivation.
• Likelihood
to
buy,
Churn
propensity...

17. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
17

18. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
18
Current, 33%
Current, 31%
Current, 34%
Current, 47%
Current, 51%
Current, 56%
Current, 47%
Current, 54%
Current, 66%
Expect, 21%
Expect, 24%
Expect, 23%
Expect, 23%
Expect, 28%
Expect, 25%
Expect, 33%
Expect, 28%
Expect, 22%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Events
Semantic
annotations
Other
entities
–
phone
numbers,
part/product
numbers,
e-­‐mail
&
street
addresses,
etc.
Metadata
such
as
document
author,
publication
date,
title,
headers,
etc.
Concepts,
that
is,
abstract
groups
of
entities
Named
entities
–
people,
companies,
geographic
locations,
brands,
ticker
symbols,
etc.
Relationships
and/or
facts
Sentiment,
opinions,
attitudes,
emotions,
perceptions,
intent
Topics
and
themes
Do
you
currently
need
(or
expect
to
need)
to
extract
or
analyze...
http://altaplana.com/TA2014

19. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
19
Many
options
(text).

20. Social
Sentiment
Analysis
in
Social
Data
11
March
2015
20
Emotion
and
outcomes

21. Social
Sentiment
Analysis
in
Social
Data
Seth
Grimes
Alta
Plana
Corporation
@sethgrimes
Social
Data
and
Analytics
–
DC
March
11,
2015