Harnessing the Semantic Space of Facebook Feeling Tags In 2013 Facebook launched a feature allowing users to add a feeling tag to their posts as part of their daily interactions online. Our research leverages the text accompanying all such volunteered feeling tags in an effort to map the semantic space of ‘Facebook feelings’ as they are catalogued by the crowd. By letting the data speak for itself, a folksonomy of feelings reveal temporal and social patterns in the most commonly shared feelings. Unlike many such studies, however, we do not only focus on examining the patterns emerging from big data, but also put the expressed feelings to work using machine learning towards both the classification and detection of emotions. This paper first demonstrates that feelings expressed online self-organize along the same lines (valence and arousal dimensions) experts in psychology and emotions have organized them for decades. As we enter the debate of classifying human emotions, our analysis directly contrasts Facebook’s manifestation of feelings with prior theoretical proposals to detect both similarities and differences from past assumptions. In line with the ‘exhibitional’ nature of Facebook, we illustrate that ‘extreme’ feelings, such as excitement and anger, are expressed in even more extreme levels of both valence and arousal. Beyond contrasting the folksonomy of feelings with dimensional mappings of emotions proposed by past research, we further utilize artificial intelligence techniques towards building a test version of an automatic ‘Feelings Meter’ able to detect feelings from text.

1. EMERGENCE OF THINGS FELT Harnessing the Semantic Space
of Facebook Feeling Tags
Chris Zimmerman Mari-Klara Stein Daniel Hardt Ravi Vatrapu

2. EMERGENCE OF THINGS FELT

5. DATASET
143 Different Feelings Collected
(all public posts from April 2013-November 2014)
11,908,715 Total posts
10,290,216 Discursive (mentions)
1,618,499 Feelings-Tagged posts
55.85% feelings tagged individually (90,3919) compared
with 89.70% in discursive mentions.

6. HARNESSING THE SEMANTIC SPACE
1. To understand feelings that users choose to explicitly tag and publicly share.
2. To map the semantic space of ‘Facebook feelings’.
3. To explore how (if at all) do the user-categorized ‘Facebook feelings’ differ,
on the valence and arousal dimensions, from previously theorized mappings
of feelings (Russell, 1983; Scherer 2005)
4. To inform organizational practices related to social media analytics
(Holsapple et al. 2014), particularly sentiment analysis (cf. Stieglitz and
Dang-Xuan 2013).
5. To build better analytics tools that are able to process data on a more
granular level and reveal more about user sentiment than just its polarity in
terms of positivity or negativity.

7. BACKGROUND
FEELINGS AND EMOTIONS
1. An online experience (e.g., ‘digital emotion’) is not inferior to
or less valid than an offline experience (Ellis and Tucker 2015).
2. Facebook is “allowing people to produce new and innovative
emotional solutions” (Ellis and Tucker, 2015, p. 178)
3. Most studies of discrete feelings have to date relied on ‘small
data’ and experimental or qualitative methods (cf. Scherer,
2005).
4. ‘Big Data’ studies and NLP techniques are popular using
traditional sentiment analysis (Barnaghi et al. 2015).
• Emotion : “an episode of
interrelated, synchronized
changes in the states of all or
most of the five organismic
subsystems (cognitive,
neurophysiological,
motivational, motor expression
and subjective feeling) in
response to the evaluation of
an external or internal stimulus
event as relevant to major
concerns of the organism”
(Scherer 2005: 697).

8. DIMENSIONAL
APPROACH
The Dimensional Approach:
(Wilhelm Wundt, 1905)
o Valence (horizontal axis)
o Arousal (vertical axis)
o Tension – often excluded

9. MEASURING
FEELINGS
Measurable Feeling:
An experience “that is an integral blend
of hedonic (pleasure–displeasure) and
arousal (sleepy–activated) values”
(Russell 2003, p. 147).
Any feeling, thus, can be described as a
point in the valence-arousal space
(ibid.; Scherer 2005).
Russel & Scherrer’s 2D classifications are
combined (right).

10. THE FACEBOOK DOMAIN
Feelings and Social Media
• Many familiar concepts on social media are not
always the same as outside of social media.
• Social media facilitate AND influence the
generation of feelings - for example through
emotional contagion. (Kramer et al. 2014).
Platform Selection – Advantages and Limitations
(-) Public Data Only / Spam
(+) Adoption / Inhabitance
(+) Personal Nature of Network
(+) Contextual Disambiguity
Mention Post
Tagged Post

11. DATA COLLECTION

12. DATA MINING: UNDERSTANDING FACEBOOK FEELINGS

13. VOLUME
Tagged Feelings
vs discursive mentions
(thin lines)

14. PROPORTIONAL
SIGNIFICANCE
Tagged Feelings
Relative to how much they are talked about (discursively)

15. HAPPY
Hourly mentions over time.

16. FEELINGS DISCUSSED ON WEEKDAYSSunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
0K
1K
2K
Challenged
0K
10K
20K
Fresh
0
500
1000
1500
Drunk
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
0K
5K
10K
Confident
0K
20K
40K
60K
Excited
0K
1K
2K
FedUp
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
0K
2K
4K
Beautiful
0
100
200
300
400
Busy
0K
1K
2K
3K
Homesick
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
0
500
1000
Grumpy
0K
5K
10K
Frustrated
0K
5K
10K
15K
Hopeless
Weekday Mentions from Sunday to Saturday

17. FACEBOOK TAG VARIATIONS
Social Dimensions - four possibilities:
- feeling happy
- feeling happy with Ravi
- feeling happy with Ravi and Dan
- feeling happy with Mari and X others
Location Dimensions – two possibilities:
- feeling happy at Warpigs
- feeling happy in Copenhagen

18. FEELINGS WITH OTHERS
Average: 1.1 actors
Small Groups
2 people 142,871 8.83%
3 people 38,119 2.36%
Large Groups
4 or more 316,795 19.57%

19. FEELINGS ON LOCATION
77,112 AT PLACE (4.76%) 19,351 IN REGION (1.20%)

20. MACHINE LEARNING: CREATING EMOTION DETECTION

21. MACHINE LEARNING

22. BINARY APPROACH
Natural Language
Processing Steps:
1. Individual Feelings
Classification (44)
2. Valence and Arousal
Classifiers (Binary
Approach)
3. 5-Way Emotion
Detection (based on
parent-child hierarchies)

23. CLASSIFIERS
Arousal and Valence

24. FEELINGS
FOLKSONOMY
Facebook Feelings Tags, as generated
by the crowd.
1. feelings of excitement are the most
widely shared
2. positive-aroused feelings hold the
most 'gravitational pull’ in general
3. there are few motivations to
express neutrally-valenced
feelings with moderate levels of
arousal
4. on the valence spectrum, the most
negative feeling is that of sadness,
greater than disappointment,
anger or even disgust

25. HOW CLASSIFICATIONS MEASURE UP

26. JUXTAPOSITION
Top-down vs Bottom-Up
1. the two-dimensional valence-
arousal space of ‘Facebook
feelings’ is qualitatively
different from prior research
(cf. Russell, 1983; Scherer,
2005);
2. yet variance between domain
theorists (ibid.) is much higher
than their individual variance
with our empirical
classification of ‘Facebook
feelings’.
3. extreme and mild feelings
tend to be exaggerated
on Facebook;

27. CONSTELLATIONS

28. GREAT! WHAT CAN WE USE THIS FOR?

29. BUILDING A FEELINGS METER
Organizational Relevance
­ more informative classifications (than
traditional sentiment analysis)
­ better monitoring of the large conversation
streams that revolve around brands online
Owned Content Conversation
• Brand Positioning
• Emotional Alignment
Earned Conversation
• Conversation Monitoring (via Radar)
• Detection from the Crowd (via Alerts)
Feelings Meter Demo Version: cssl.cbs.dk/software/feelingsmeter)
5-Way Emotion Detection

30. FIFA FACEBOOK WALL
However a significant spike in collective negativity is strongly apparent.
Arousal level of the conversation remains relatively unchanged. Surge in anger occurs after FIFA executives are arrested.
Fluctuations in joy levels seem to stabilize after the event.

31. LEVERAGING A
WISDOM OF CROWDS
The nature of Facebook data also allows us to draw on the folksonomic wisdom
of the crowds via several advantages:
• The sheer volume of posts allows us to leverage the contributions of the English
speaking population who volunteer feeling tags as they see appropriate.
• Facebook feelings are collected across time and space, within a natural
inhabitation online. Traditional survey studies are performed at specific times
and spaces, not allowing subjects to appropriate emotions in an embedded
fashion at any and every point in time in their daily lives.
• Our data-driven approach from big social data has allowed the patterns in
feeling tag use to emerge from millions of posts, letting the data speak for
itself, and to reveal observable differences from past assumptions.

32. Christopher Zimmerman
Computational Social Science Lab
Copenhagen Business School – ITM
twitter @socialbeit cz.itm@cbs.dk
EMERGENCE OF THINGS FELT Harnessing the Semantic Space
of Facebook Feeling Tags
Chris Zimmerman Mari-Klara Stein Daniel Hardt Ravi Vatrapu ICIS 2015, Dallas, Texas
Feelings Meter Demo Version:
cssl.cbs.dk/software/feelingsmeter