Valence, Arousal and Dominance Estimation for English, German ...

Valence, Arousal and Dominance Estimation for English, German, Greek, Portuguese and Spanish Lexica using Semantic Models Elisavet Palogiannidi, Elias Iosif, Polychronis Koutsakis, Alexandros Potamianos Technical University of Crete, Chania, Crete, Greece National Technical University of Athens, Zografou, Athens, Greece “Athena” Research and Innovation Center, Maroussi, Athens, Greece

Interspeech 2015

Goals and Motivation

Semantic - Affective Model

Experiments and Results

Conclusions and Future Work

Elisavet Palogiannidi, Elias Iosif, Polychronis Koutsakis and Alexandros Potamianos

Interspeech 2015, Dresden, Germany

E. Palogiannidi, E. Iosif, P. Koutsakis, A. Potamianos

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Presentation Outline

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Goal and Motivation

Goal:Assign continuous high quatlity affective scores on words, using semantic features, for multiple languages Motivation Semantic similarity implies affective similarity Affective text labelling at the core of many multimedia applications e.g., Sentiment analysis, Spoken dialogue systems (SpeDial), Emotion tracking of multimedia content

Create affective lexica with large vocabulary coverage Affective scores of larger than words lexical units can rely on words’ affective scores



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Prior Work

Affective model: Extension of [Turney and Littman, 2002], proposed by [Malandrakis et al. 2013b] The semantic model is built, based on the corpus Training phase for the semantic to the affective mapping Affective lexica are used for the training, e.g., ANEW [Bradley and Lang 1999]

[Malandrakis et al. 2014]



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Semantic similarity

Semantic similarity of words Builds the semantic model Expresses the degree in which two words have “similar meaning” with each other

Context based semantic similarities “Similarity of context implies similarity of meaning” Given a vocabulary V and a corpus, each wi ∈ V , i = 1 · · · |V |, is represented by a contextual feature vector

Contextual feature vector of a word wi Encodes the linguistic context of wi



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Prior work applications

The affective model Has been applied to tweets or sms [Malandrakis et al. 2013b] and news headlines [Malandrakis et al. 2013a] Is applicable to words or n-grams [Malandrakis et al. 2013b] Works for numerous dimensions [Malandrakis and Narayanan], e.g Valence, Arousal, Dominance, Concreteness, Imagability, Familiarity, Gender Ladenness

In this work, we predict Valence, Arousal and Dominance of words 1 2 3

Valence: ranges from negative to positive Arousal: ranges from calm to activated Dominance: ranges from controlled to controller



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The big picture



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Outline

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Affective model [Malandrakis et al. ’13] Requires a small, manually annotated affective lexicon for bootstrapping Assumption: The affective score of a word can be expressed as a linear combination of the affective ratings of seed words weighted by semantic similarity and trainable weights αi υˆ(wj ) = α0 +

N X

αi υ(wi )S(wj , wi )

(1)

i=1

υˆ(wj ): estimated affective rating of the unknown word wj w1..N : seed words υ(wi ): affective rating of wi (valence, arousal or dominance) αi : weight assigned to wi (α0 : bias) S(·): semantic similarity between wj and wi Interspeech 2015, Dresden, Germany


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Semantic - affective mapping

Not all seeds are equally salient Weights estimation (α0 · · · αN ) through supervised learning  1  1 1

S(w1 , w1 )υ(w1 ) .. . S(wK , w1 )υ(w1 )

··· .. . ···

     1 S(w1 , wN )υ(wN ) α0  υ(w1 )    .    ..  ·  ..  =  ..  .  .  S(wK , wN )υ(wN ) αN υ(wK )

(2)

A system of K linear equations with N + 1 (N < K ) unknown variables is solved using Least Squares Estimation (LSE ) Ridge Regression (RR)



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Semantic Model

Distributional semantic models (DSMs) framework “Words with similar context tend to be semantically related” Contextual windows that contain words or character n-grams

Weighting of contextual features (words or char. n-grams): 1

Binary:

2

PPMI:

1 if feature occurs in wi ’s context else 0 Positive pointwise mutual information between feature and wi

Semantic similarity between two words Estimated as the cosine of their contextual feature vectors



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Experimental Procedure - Data Goal Estimate the affective scores of words in multiple languages Three affective dimensions: Valence, Arousal, Dominance Five languages: English, German, Greek, Portuguese, Spanish For each language: A web harvested corpus A vocabulary and a bootstraping affective lexicon

Semantic similarity computation Words (W) and character n-grams contextual features Binary (B) and PPMI weighting schemes Fusion: combine different types of contextual feature vectors



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Experimental Procedure - Set up and evaluation metrics

Main parameters of interest Number of seeds Semantic similarity metric

Evaluation datasets The affective lexica of each language 10-fold cross validation: 90% train and 10% test The seeds are selected in order to create a balanced set

Evaluation Metrics Pearson Correlation between the manually annotated and the automatically estimated scores Binary classification accuracy (positive vs. negative values)



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Performance as a function of the seeds

Valence classification accuracy

Classification Accuracy

English

Greek

German

Portuguese

Spanish

0.9

0.85

0.8

0.75

0.7

0

100

200

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400

500

600

Number of seeds



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Comparison of evaluation metrics

Valence evaluation of five languages English

Greek

German

Portuguese

Spanish


0.9

Correlation

0.85

0.8

0.75

0.7

0.65 0

100

200

300

Number of seeds


400

500

600

0.9

0.85

0.8

0.75

0.7

0

100

200

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400

500

600

Number of seeds


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Comparison of affective dimensions Valence (a), Arousal (b), Dominance (c) clas. accuracy Greek

German

Portuguese

Spanish

0.9

0.9



English

0.85

0.8

0.75

0.85

0.8

0.75

0.7

0.65

0.7

0

100

200

300

400

500

600

0

Number of seeds

100

200

300

Number of seeds

(a)

400

500

600

(b)


0.9

0.85

0.8

0.75

0.7

0.65

0

100

200

300

Number of seeds


400

500

600

(c)


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Comparison of RR and LSE Arousal

0.4 0.2 10

200

400

600

900


Correlation

Arousal 0.8 0.7 0.6

0.8 0.75 0.7 0.65

Number of seeds Spanish − RR

Spanish − LSE

10

200

400

600

Number of seeds

Greek − LSE

900

Greek − RR

Using RR with the appropriate λ Performance stays robust for a large number of seeds

RR improves performance of Greek and Spanish on Arousal Interspeech 2015, Dresden, Germany


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Valence of different semantic similarity metrics (1/2)

Contextual windows include Words (W) Character n-grams (4-gram) Combination of the above

Binary (B) or Pointwise Mutual Information (PPMI) weighting scheme Using 600 seeds and LSE We show class. accuracy (correlation results are similar)



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Valence of different semantic similarity metrics (2/2) PPMI works better than binary

Sem. Similarity W-B W-PPMI


English 86.9 90.9

Greek 84.3 87.6

Spanish 85.9 85.3

Portuguese 89.3 90.8


German 77.1 85.2

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Valence of different semantic similarity metrics (2/2) PPMI works better than binary Character n-grams work equally well with words

Sem. Similarity W-B W-PPMI 4gram-PPMI


English 86.9 90.9 89.8

Greek 84.3 87.6 87.5

Spanish 85.9 85.3 87.7

Portuguese 89.3 90.8 87.4


German 77.1 85.2 82.6

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Valence of different semantic similarity metrics (2/2) PPMI works better than binary Character n-grams work equally well with words Concatenating different contextual vectors does not improve the performance Sem. Similarity W-B W-PPMI 4gram-PPMI W/4gram-PPMI


English 86.9 90.9 89.8 90.5

Greek 84.3 87.6 87.5 87.2

Spanish 85.9 85.3 87.7 87.9

Portuguese 89.3 90.8 87.4 89.3


German 77.1 85.2 82.6 83.0

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Valence of different semantic similarity metrics (2/2) PPMI works better than binary Character n-grams work equally well with words Concatenating different contextual vectors does not improve the performance Sem. Similarity W-B W-PPMI 4gram-PPMI W/4gram-PPMI

English 86.9 90.9 89.8 90.5

Greek 84.3 87.6 87.5 87.2

Spanish 85.9 85.3 87.7 87.9

Portuguese 89.3 90.8 87.4 89.3

German 77.1 85.2 82.6 83.0

Weighting scheme is the most important parameter English achieves highest performance German achieves highest performance increase Char. 4-gram-PPMI works almost always better than W-B Interspeech 2015, Dresden, Germany


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Conclusions Universality of affective model : Equally high performance for multiple languages Class. Accuracy ranges from 85.2% to 90.9% Best performance for English

Applied to different affective dimensions Best performance for Valence (median, mean: 87%, 88%) Arousal (median, mean: 75%, 74% ), Dominance (median, mean: 79%)

Affective model improvement by Incorporating a regularization factor in semantic-affective mapping

Character 4-grams are salient features for affective lexicon expansion



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Future Work

Ongoing work 1

Investigate the compositional aspects of emotion

2

Fusion with speech affective system (SpeDial)

From affect of words to affect of phrases, sentences Hot anger detection

Improve computation of semantic similarity e.g., investigate the role of morphology

Evaluate affective model on more languages



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References

ey and Lang 1999 M. Bradley and P. Lang, “Affective norms for English words (ANEW): Stimuli, instruction manual and affective ratings. Technical report C-1,” The Center for Research in Psychophysiology, University of Florida, 1999.

akis et al. 2013 a N. Malandrakis, A. Potamianos, E. Iosif and S. Narayanan. 2013. “Distributional Semantic Models for Affective Text Analysis”. IEEE Transactions on Audio, Speech and Language Processing.

akis et al. 2013 b N. Malandrakis, A. Kazemzadeh, A. Potamianos, and S. Narayanan. 2013 “SAIL: A hybrid approach to sentiment analysis”. SemEval 2013

drakis et al. 2014 N.Malandrakis, A. Potamianos, K. J. Hsu , K. N. Babeva, M. C. Feng , G. C. Davison , S. Narayanan, 2014 “Affective Language Model Adaptation Via Corpus Selection”, ICASSP 2014

is and Narayanan N. Malandrakis, and S. Narayanan. 2015 “Therapy Language Analysis using Automatically Generated Psycholinguistic Norms” Interspeech 2015

and Littman 2002 P. Turney and M. L. Littman, “Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus. Technical report ERC-1094 (NRC 44929),” National Research. Council of Canada, 2002.



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