EMOBANK: Studying the Impact of Annotation Perspective and

Representation Format on Dimensional Emotion Analysis

Sven Buechel and Udo HahnJena University Language & Information Engineering (JULIE) Lab

Friedrich-Schiller-Universität Jena, Jena, Germany{sven.buechel,udo.hahn}@uni-jena.de

http://www.julielab.de

Motivation Valence-Arousal-Dominance (VAD)

−1.0 −0.5 0.0 0.5 1.0−1.0

−0.5

0.0

0.5

1.0

−1.0

−0.5

0.0

0.5

1.0

●

●

●●

●

●

Anger

SurpriseDisgust

Fear

Sadness

Joy

Valence(displeasure—pleasure)

Dom

inan

ce(b

eing

con

trolle

d—in

con

trol)

• Build large-scale gold standard for novel emotion representation (VAD)

• Compare reader and writer perspective • Enable mapping between emotion formats

Bi-Perspectival Design

Bi-Representational Design

Corpus Acquisition

Conclusion• Largest multi-annotated emotion corpus

• for more than one perspective• for more than one emotion format• at all (10k sentences)

• Reader perspective turned out superior• Mapping VAD to Basic Emotions reaches human

annotation capacity• Available: https://github.com/JULIELab/EmoBank

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−0.5 0.0 0.5

−0.5

0.0

0.5

Difference in Emotionality

Diff

eren

ce in

Erro

r

Figure 3: Differences in emotionality and differ-ences in error between WRITER and READER,each sentence corresponding to one data point; re-gression line depicted in red.

ing between the perspectives), the more they dif-fer in error as well. Running linear regression onthese two data rows, we find that the regressionline runs straight through the origin (intercept isnot significantly different from 0; p = .992; seeFigure 3). This means that without difference inemotionality, WRITER and READER rating for asentence do, on average, not differ in error. Hence,our data strongly suggest that READER is the su-perior perspective yielding better inter-annotatorcorrelation and emotionality without overpropor-tionally increasing inter-annotator error.

5 Mapping between Emotion Formats

Making use of the bi-representational subset ofour corpus (SE07), we now examine the feasibil-ity of automatically mapping between dimensionaland categorical models. For each Basic Emotioncategory, we train one k Nearest Neighbor modelgiven all VAD values of either WRITER, READERor both combined as features. Training and hyper-parameter selection was performed using 10-foldcross-validation.

Comparing the correlation between our models’predictions and the actual annotations (in categor-ical format) with the IAA as reported by Strap-parava and Mihalcea (2007), we find that this ap-proach already comes close to human performance(see Table 3). Once again, READER turns out to besuperior in terms of the achieved mapping perfor-mance compared to WRITER. However, both per-spectives combined yield even better results. Inthis case, our models’ correlation with the actualSE07 rating is as good as or even better than theaverage human agreement. Note that the SE07 rat-ings are in turn based on averaged human judg-ments. Also, the human IAA differs a lot between

Joy Ang Sad Fea Dsg Srp Av.IAA .60 .50 .68 .64 .45 .36 .54W .68 .40 .67 .47 .27 .15 .44R .73 .47 .68 .54 .36 .15 .49WR .78 .50 .74 .56 .36 .17 .52DW +.08 –.10 –.01 –.17 –.17 –.21 –.09DR +.13 –.03 +.00 –.10 –.09 –.22 –.05DWR +.18 +.00 +.05 –.08 –.09 –.19 –.02

Table 3: IAA by Strapparava and Mihalcea (2007)compared to mapping performance of KNN mod-els using writer’s, reader’s or both’s VAD scoresas features (W, R and WR, respectively), both inPearson’s r. Bottom section: difference of respec-tive model performance (W, R and WR) and IAA.

the Basic Emotions and is even r < .5 for Dis-gust and Surprise. For the four categories witha reasonable IAA, Joy, Anger, Sadness and Fear,our best models, on average, actually outperformhuman agreement. Thus, our data shows that au-tomatically mapping between representation for-mats is feasible at a performance level on par withor even surpassing human annotation capability.This finding suggests that, for a dataset with high-quality annotations for one emotion format, auto-matic mappings to another format may be just asgood as creating these new annotations by manualrating.

6 Conclusion

We described the creation of EMOBANK, thefirst large-scale corpus employing the dimensionalVAD model of emotion and one of the largest goldstandards for any emotion format. This genre-balanced corpus is also unique for having twokinds of double annotations. First, we annotatedfor both writer and reader emotion; second, for asubset of the EMOBANK, ratings for categoricalBasic Emotions as well as VAD dimensions arenow available. The statistical analysis of our cor-pus revealed that the reader perspective yields bothbetter IAA values and more emotional ratings. Forthe bi-representationally annotated subcorpus, weshowed that an automatic mapping between cat-egorical and dimensional formats is feasible withnear-human performance using standard machineleraning techniques.

Acknowledgments

We thank The Center for the Study of Emotion andAttention, University of Florida, for granting usaccess to the Self-Assessment-Manikin (SAM).

• Raw data:• MASC � ANC (Ide et al., 2010)• SemEval-2007 Task 14 (Strapparava & Mihalcea, 2007)Ø Genre-balanced

• Annotate full corpus according to reader and writer perspective. Pilot: Buechel & Hahn (2017)

• 5 raters per sentence and perspective (CrowdFlower)• Reader: better correlation

but worse error IAA • Also more emotional

ratings• Emotionality correlates

with error• Increased error explained

by higher intensity

Correlation- (r ) and Error- (MAE) based IAA

GenreDistribution

• Previous studies hard to compare (incompatible formats)Ø Can we automatically map between formats?Ø Train kNN models to predict Basic Emotions given VADØ Writer and reader combined reaches human IAA

Measuring EmotionCorpus Domain Raw FilteredSE07 news headlines 1,250 1,192

MASC

blogs 1,378 1,336essays 1,196 1,135fiction 2,893 2,753letters 1,479 1,413newspapers 1,381 1,314travel guides 971 919

Sum 10,548 10,062

Table 1: Genre distribution of the raw and filteredEMOBANK corpus.

Second, we conducted a pilot study on two sam-ples (one consisting of movie reviews, the otherpulled from a genre-balanced corpus) to comparethe IAA resulting from different annotation per-spectives (e.g., the writer’s and the reader’s per-spective) in different domains (see Buechel andHahn (2017) for details). Since we found differ-ences in IAA but the results remained inconclu-sive, we decided to annotate the whole corpus bi-perspectivally, i.e., each sentence was rated ac-cording to both the (perceived) writer and readeremotion (henceforth, WRITER and READER).

Third, since many problems of comparing emo-tion analysis studies result from the diversity ofemotion representation schemes (see Section 2),the ability to accurately map between such alterna-tives would greatly improve comparability acrosssystems and boost the reusability of resources.Therefore, at least parts of our corpus should beannotated bi-representationally as well, comple-menting dimensional VAD ratings with annota-tions according to a categorical emotion model.

Following these criteria, we composed our cor-pus out of several categories of the ManuallyAnnotated Sub-Corpus of the American NationalCorpus (MASC; Ide et al. (2008), Ide et al. (2010))and the corpus of SemEval-2007 Task 14 AffectiveText (SE07; Strapparava and Mihalcea (2007)).MASC is already annotated on various linguisticlevels. Hence, our work will allow for researchat the intersection of emotion and other languagephenomena. SE07, on the other hand, bears anno-tations according to Ekman’s six Basic Emotion(see Section 2) on a [0, 100] scale, respectively.This collection of raw data comprises 10,548 sen-tences (see Table 1).

Given this large volume of data, we opted fora crowdsourcing approach to annotation. Wechose CROWDFLOWER (CF) over AMAZON ME-CHANICAL TURK (AMT) for its quality controlmechanisms and accessibility (customers of AMT,

Sven%Büchel%—%JULIE%LAB%(Prof.%Dr.%Udo%Hahn)%—%FSU%Jena%—%November%2,%2016%

�

Pleasure(

Unhappy%Annoyed%Unsatisfied%

% % % % %

Happy%Pleased%Satisfied%

%

% % % % % % %% % % % % % %

Arousal(

Calm%Relaxed%Sleepy%

%% % % % %

Excited%Nervous%Aroused%

%

% % % % % % %% % % % % % %

Control(

Submissive%Influenced%Guided%

%

% % % % %

Dominant%In%control%Influential%

% % % % % % %%

Sven%Büchel%—%JULIE%LAB%(Prof.%Dr.%Udo%Hahn)%—%FSU%Jena%—%November%2,%2016%

�

Pleasure(

Unhappy%Annoyed%Unsatisfied%

% % % % %

Happy%Pleased%Satisfied%

%

% % % % % % %% % % % % % %

Arousal(

Calm%Relaxed%Sleepy%

%% % % % %

Excited%Nervous%Aroused%

%

% % % % % % %% % % % % % %

Control(

Submissive%Influenced%Guided%

%

% % % % %

Dominant%In%control%Influential%

% % % % % % %%

Sven%Büchel%—%JULIE%LAB%(Prof.%Dr.%Udo%Hahn)%—%FSU%Jena%—%November%2,%2016%

�

Pleasure(

Unhappy%Annoyed%Unsatisfied%

% % % % %

Happy%Pleased%Satisfied%

%

% % % % % % %% % % % % % %

Arousal(

Calm%Relaxed%Sleepy%

%% % % % %

Excited%Nervous%Aroused%

%

% % % % % % %% % % % % % %

Control(

Submissive%Influenced%Guided%

%

% % % % %

Dominant%In%control%Influential%

% % % % % % %% Figure 2: The modified 5-point Self-Assessment

Manikin (SAM) scales for Valence, Arousal andDominance (row-wise). Copyright of the originalSAM by Peter J. Lang 1994.

but not CF, must be US-based). CF’s main qual-ity control mechanism rests on gold questions,items for which the acceptable ratings have beenpreviously determined by the customer. Thesequestions are inserted into a task to restrict theworkers to those performing trustworthily. Wechose these gold items by automatically extractinghighly emotional sentences from our raw data ac-cording to JEMAS4, a lexicon-based tool for VADprediction (Buechel and Hahn, 2016). The ac-ceptable ratings were determined based on manualannotations by three students trained in linguis-tics. The process was individually performed forWRITER and READER with different annotators.

For each of the two perspectives, we launchedan independent task on CF. The instructions werebased on those by Bradley and Lang (1999) towhom most of the VAD resources developed inpsychology refer (see Section 2). We changed the9-point SAM scales to 5-point scales (see Figure2) in order to reduce the cognitive load during de-cision making for crowdworkers. For the writer’sperspective, we presented a number of linguis-tic clues supporting the annotators in their ratingdecisions, while, for the reader’s perspective, weasked what emotion would be evoked in an aver-age reader (rather than asking for the rater’s per-sonal feelings). Both adjustments were made toestablish more objective criteria for the exclusionof untrustworthy workers. We provide the instruc-tions along with our dataset.

For each sentence, five annotators generatedVAD ratings. Thus, a total of 30 ratings were gath-ered per sentence (five ratings for each of the threeVAD dimensions and two annotation perspectives,WRITER and READER). Ten sentences were pre-sented at a time. The task was available for work-

4https://github.com/JULIELab/JEmAS

Self-Assessment

Manikin(Bradley & Lang, 1994)

Corpus Domain Raw Filtered

MASC

blogs 1,378 1,336essays 1,196 1,135fiction 2,893 2,753letters 1,479 1,413newspapers 1,381 1,314travel guides 971 919

SemEval07 news headlines 1,250 1,192Sum 10,548 10,062

Valence Arousal Dominance Averager-writer 0.698 0.578 0.540 0.605r-reader 0.738 0.595 0.570 0.634

MAE-writer 0.300 0.388 0.316 0.335MAE-reader 0.349 0.441 0.367 0.386

ReferencesBradley & Lang. 1994. Measuring emotion: The self-assessment manikin and the semantic differential. Journal of

Behavior Therapy and Experimental Psychiatry, 25(1):49–59. Buechel & Hahn. 2017. Readers vs. writers vs. texts: Coping with different perspectives of text understanding in

emotion annotation. In LAW 2017. Ide, Baker, Fellbaum & Passonneau. 2010. The Manually Annotated Sub-Corpus: A community resource for and by the

people. In ACL 2010.Strapparava & Mihalcea. 2007. SemEval-2007 Task 14: Affective text. In SemEval 2007.

Joy Anger Sad. Fear Disg. Surp. Avg.IAA .60 .50 .68 .64 .45 .36 .54Writer .68 .40 .67 .47 .27 .15 .44Reader .73 .47 .68 .54 .36 .15 .49Writer&Reader .78 .50 .74 .56 .36 .17 .52Diff Writer – IAA +.08 –.10 –.01 –.17 –.17 –.21 –.09Diff Reader – IAA +.13 –.03 +.00 –.10 –.09 –.22 –.05Diff W&R – IAA +.18 +.00 +.05 –.08 –.09 –.19 –.02