EMOTION TEXT INFERENCE TOOL FOR VISUAL ANALYTICS

ICIS Pre-workshop on Text Mining as a Strategy of Inquiry in Information Systems ResearchDublin 11/12/2016

1. Problem Space

2. Purpose

3. Emotions Research (ICIS 2015)

4. Tool Development (DESRIST 2016)

5. Live Demo : Chipotle

6. Examples of Application : Denmark.dk

7. Text Mining Methodology & Feedback

ICIS 2015 / HARNESSING THE SEMANTIC SPACEDESRIST 2016 / BUILDING A FEELINGS METER

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 an analytics tool capable of processing emotions on a more granular level and reveal more about crowd sentiment; a tool that can easily be incorporated into researcher and practitioner workflows.

SENTIMENT ANALYSIS

34% POS 12% NEG 25% NEU

SENTIMENT ANALYSIS

34% POS 12% NEG 25% NEU

SENTIMENT ANALYSIS

34% POS 12% NEG 25% NEU

CAPTURING EMOTIONSICIS 2015 / Harnessing the Semantic Space

EMERGENCE OF THINGS FELT

VOLUMETagged Feelings

vs discursive mentions (thin lines)

HAPPY

Hourly mentions over time.

FEELINGS DISCUSSED ON WEEKDAYSSunday

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Weekday Mentions from Sunday to Saturday

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%

FEELINGS ON LOCATION77,112 AT PLACE (4.76%) 19,351 IN REGION (1.20%)

DIMENSIONALAPPROACH

The Dimensional Approach:

(Wilhelm Wundt, 1905)

o Valence (horizontal axis)

o Arousal (vertical axis)

o Tension – often excluded

FEELINGSFOLKSONOMY

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-valencedfeelings 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

CONSTELLATIONS

BUILDING A FEELINGS METERDESRIST 2016 / EMOTIONVIS

TOOL DEMOChipotle Facebook Wall

CHALLENGES

q collecting large training sets

q cleaning social data

q training with many leakages

q forming a data-driven typology

q balanced v unbalanced classifier

q non-emotional class (having an emotionality threshold)

EMOTION V NON-EMOTION

How to impose a threshold of emotionality?

q Detect non-emotion - find a non-emotional dataset

q Detect ‘emotionality’ - use an existing dictionary (LIWC, etc),

q OR use feature list from our very large dataset pf emotion tags (with a large empirical foundation)

1. Sort - arrange all the words (features) by how discriminative they are of a certain class (emotion categories)….

2. Order - sorted by the productiveness of each feature.

3. Define - threshold across to impose across the board

TOP 20 - CORE EMOTIONS

Word Coefficientmiss 6.986016894rip 5.141740386

rest 4.066502484sad 3.721818005

missing 3.69276163heart 3.176140494

devastated 3.112709379pain 3.018786778

heaven 2.822618092peace 2.629398536

help 2.600327012accident 2.570705668

missed 2.554370967ashamed 2.481876802

gone 2.469852326vibestreet 2.428147187grandma 2.425137259

lost 2.376184404hurt 2.374763895

anymore 2.31673515

Word Coefficientshame 4.817227383

fucking 4.624291437hate 4.400117991

angry 4.244185619trick 3.904443896

police 3.478886064stupid 3.357348258

fuck 3.205525077pissed 3.176277029

hell 3.109560947ki 3.046973127

boycott 2.961132313seized 2.905431273

government 2.895995141snuggle 2.860197067

israel 2.799715298ct 2.797375719

stolen 2.714096689killed 2.658487516

georghiou 2.635029566

Word Coefficientlol 7.11841604

lmao 5.232171744funny 4.704103552haha 3.520833606

lmfao 3.250737286silly 3.209428659

pubic 3.075086933hahaha 3.053358807

laughing 3.042177409hilarious 2.771143801brilliant 2.621092263

humor 2.556675867boa 2.441085167

ce 2.40026206claudia 2.395111804

kkkkk 2.367743641weber 2.355344547

hahahaha 2.330192825saw 2.278566134

oh 2.274346493

Word Coefficientchallenge 5.057432269

finished 4.105620733proud 3.859225257finally 3.821250998

starring 2.986186739workout 2.900317902congrats 2.745343277

productive 2.68759694confidence 2.682003006

glory 2.547624651barely 2.505130637

com 2.488742175accomplished 2.477173108

completed 2.461110115working 2.42237729

sweat 2.366149229ready 2.352519136work 2.256495611

niggaz 2.148209683officially 2.12516032

ANGER EMPOWERED EXCITED SADNESS

Word Coefficientconfused4.434192281

omg4.269047991scary3.607214015

worried3.468052141seen 3.12186108safe3.116267323

ebola3.008196773animal2.984916385

pray2.831980246scared 2.83058258

continued2.825664009shocked2.806177611

ghost2.606510039separated 2.60014044

alert2.551586858thoughts2.485203644

comments 2.41972063otha2.394228131

praying2.349163576a4383222.334662301

FEARWord Coefficient

awesome 4.361401439happy 3.194259797

pm 3.187567352club 2.81594721

great 2.699492084button 2.597035291

hhhmmm 2.559250826enjoy 2.47965485

weekend 2.409352193mall 2.364866299best 2.328404284

sir 2.324124295available 2.297224631

coach 2.268088716india 2.22553258

apple 2.225355098team 2.1633253572013 2.137368371

mr 2.110431081amazing 2.110370313

JOYFUL

APPLICATION EXAMPLEDENMARK.DK

CONVERSATION OBJECTIVES

§ Democracy§ Education§ Happiness§ Welfare§ Work (and Life-Balance)§ Rule of Law

The nations of Denmarkand Sweden had aTwitter fight involvingmoose and spermbanksUpdated by Zack Beauchamp on July 7, 2016, 12:10 p.m. ET

@zackbeauchamp zack@vox.com

This, basically. (Khosro/Shutterstock)

GOING TO WAR WITH SWEDEN

Conversation networks can be traced to see the spread of dialogue and identify influencers or groups (cliques)

TIT-FOR-TAT ATTACKS

Overall, content by @swedense had the most echos in the full conversation dataset.

Some posts reached over 400 instances.

FACEBOOK DATASETS

16,233 total contributions

2,283 published pieces (admin posts)

13,950 public reactions (comments)

Isolating the page discourse from the community discussion allows us to contrast the topics discussed, emotional tone, and general behaviour between your actions and that of the reactions from the crowd.

The entire Facebook wall was collected to zoom out and listen to the community’s discourse and reactions for almost 9 years.

2008-16 full history of Facebook wall

> The most active day ever was on April 26, 2016

ACTION & REACTION• Admin posts have been

steadily declining over the past 5 years.

• The current rate has been between 10 and 20 posts per month in the past two years.

• The community however have been commenting more and more in the past three years especially. Some months recently have reached up to 500 user comments.

Publ

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2,283 published pieces (admin posts)

13,950 public reactions (comments)

NEGATIVITY

Sentiment levels show a few days with positive and negative swings, which seem to be happening at a more frequent rate in the past two years.

April 9th 2014 saw the lowest level of sentiment thus far.

AROUSAL• Arousal has been climbing from the community. The number of days that say a spike in arousal levels

seems to be increasing in the last three years. • April 26th 2016 saw the highest levels of arousal that the Facebook community has experienced yet.

AROUSAL

When we zoom in to just the event days (July 7-9), arousal peaked on July 7th, before the surge in volume.

EMOTION ANALYSIS

Joy is the single greatest emotion detected from the Denmark.dk community on Facebook (37.8%), followed closely by excitement. Emotionality itself peaked on April 16, 2015, with birthday greetings from the crowd on Queen Magrethe’s 75th birthday, consisting of mostly joy and excitement.

JOY• Feeling ‘happy’, ‘fantastic’ and super were detected most, while ‘hopeful’ joy

much less.• Within joyous posts, commonly used terms offer clues as to why Joy is detected

so much. These in include family, people, life, Copenhagen and visit.

MOST ACTIVE FACEBOOK USERS

1,717 unique actors have taken part in the conversation over the past 8 years.

After removing spam posts (comments over 500 characters), a handful of people have contributed the most (right) and are consider the most vocal members of the community.

WHO EXHIBITED WHICH EMOTIONS THE MOSTOne can also see whose comments have been the most Angry or Sad over time, for example. Certain individuals may be of importance to be aware of.

EMOTIONAL ALIGNMENT

Crowd Reaction - Comments

Page Dialogue - Posts

Emotional signatures from the publication content and the community are similar, but slightly different. Admin posts taken on an 44% excited tone, which is more muted in the community who are just as happy (38%) as the page content, but have a more significant amount of sadness and anger.

Comparing with the Crowd

EVOLUTION OVER TIME

emotionality trenddominating emotions

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Emotionality in general has been rising in admin published posts. April 16, 2016 was an outlier in terms of high degrees of emotionality by the community.

The admin published stories have consistently been excited (orange) and happy (green) over time whereas the community have had a greater degree of sadness (blue) consistently.

OPPORTUNITY

q With three dimensions (valence, arousal, and distinct emotion) there is a far better triangulation of the conversational mood overall.

q Coarse and fine-grain emotion categorization offer greater contextual depth than valence.

q By visualizing these classifications in detail we can map emotional signatures of conversations.

q By combining the classifications with other dimensions (time, actors and topical spaces) we can empower practitioners to make meaning and take action.

Chris Zimmermantwitter : @socialbeit

chzi10ab@student.cbs.dk

Ravi Vatrapu Mari-Klara Stein Daniel HardtCopenhagen Business School - Department of IT Management