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Geometric and Statistical Analysis of Topics and Emotions in Corpora 
Francesco Tarasconi - tarasconi@celi.it 
Vittorio Di...
Introduction: Analysis of Emotions 
Francesco Tarasconi and Vittorio Di Tomaso 
2 
NLP: 
Topic detection 
Sentiment analys...
BACKGROUND
A Model of Emotions in Social Networks 
Francesco Tarasconi and Vittorio Di Tomaso 
4 
Primary emotions according to Ekman...
Social TV, the “Second Screen” 
Francesco Tarasconi and Vittorio Di Tomaso 
5 
Sharing of experiences (and emotions!) betw...
METHODOLOGY
Vector Space Model Representations 
Francesco Tarasconi and Vittorio Di Tomaso 
7 
DOCi = { topic A, topic B, ... , emotio...
Emotional Distances Between Topics 
Francesco Tarasconi and Vittorio Di Tomaso 
8 
Key elements: 
1)High variance in topic...
Simple and Multiple Correspondence Analysis 
Francesco Tarasconi and Vittorio Di Tomaso 
9 
Strong link with PCA: dimensio...
Why MCA 
Francesco Tarasconi and Vittorio Di Tomaso 
10 
1)It accounts for different volumes in the original variables (ma...
EXAMPLES
Social TV Emotional Landscape 
Francesco Tarasconi and Vittorio Di Tomaso 
12
X Factor’s Emotional Phases 
Francesco Tarasconi and Vittorio Di Tomaso 
13
MasterChef’s Quirks 
Francesco Tarasconi and Vittorio Di Tomaso 
14
X-Factor vs MasterChef 
Francesco Tarasconi and Vittorio Di Tomaso 
15
Conclusions and Further Researches 
Francesco Tarasconi and Vittorio Di Tomaso 
16 
We have shown how to represent and hig...
We would like to thank: 
V. Cosenza and S. Monotti Graziadei for stimulating these researches; 
the ISI-CRT foundation and...
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Celi @Clic2014: Geometric and Statistical Analysis of Topic and Emotions in Corpora

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La nostra seconda presentazione al CLIC 2014: "Geometric and Statistical Analysis of Topic and Emotions in Corpora", con cui Francesco Tarasconi ha vinto l'attestato di Distinguished Young Paper, dato agli 8 migliori papers del convegno con un autore giovane.

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Celi @Clic2014: Geometric and Statistical Analysis of Topic and Emotions in Corpora

  1. 1. Geometric and Statistical Analysis of Topics and Emotions in Corpora Francesco Tarasconi - tarasconi@celi.it Vittorio Di Tomaso - ditomaso@celi.it Pisa, 9/12/2014
  2. 2. Introduction: Analysis of Emotions Francesco Tarasconi and Vittorio Di Tomaso 2 NLP: Topic detection Sentiment analysis Emotion detection Many, potentially correlated, variables Role of Data Analysis: Define, visualize and understand emotional similarities Focus of the present work: background, metholodogy, examples
  3. 3. BACKGROUND
  4. 4. A Model of Emotions in Social Networks Francesco Tarasconi and Vittorio Di Tomaso 4 Primary emotions according to Ekman (1972): Anger Disgust Fear Joy Sadness Surprise Plus: Love Like Dislike © Paul Ekman. All rights reserved
  5. 5. Social TV, the “Second Screen” Francesco Tarasconi and Vittorio Di Tomaso 5 Sharing of experiences (and emotions!) between viewers of the same program Source: Blogmeter, www.blogmeter.it Emotional profiles of audiences and, by extension, of whole shows / episodes
  6. 6. METHODOLOGY
  7. 7. Vector Space Model Representations Francesco Tarasconi and Vittorio Di Tomaso 7 DOCi = { topic A, topic B, ... , emotion x, emotion y, ... } Annotated documents as vectors in a ntopic + nemotion dimensional space Document-annotation indicator matrix D TOPICi = [ frequency 1, frequency 2, ... , frequency nemotion ] Topics as vectors in a nemotion dimensional space Topic-emotion frequency matrix T IMPRESSIONi = { topic A, emotion x } Impressions as vectors in a ntopic + nemotion dimensional space Impression-annotation indicator matrix J
  8. 8. Emotional Distances Between Topics Francesco Tarasconi and Vittorio Di Tomaso 8 Key elements: 1)High variance in topic absolute frequencies 2)High variance in emotion absolute frequencies 3)A graphical representation is required 4)Why are two topics similar? A graphical representation can be obtained using by dimension reduction.
  9. 9. Simple and Multiple Correspondence Analysis Francesco Tarasconi and Vittorio Di Tomaso 9 Strong link with PCA: dimension reduction, eigenvalue methods CA (Hirschfeld, 1935) of contingency table T SVD of standardized residual matrix Principal coordinates and symmetric map Inertia and quality of the representation MCA of indicator matrix J or Burt matrix JTJ Analysis of surveys (Benzecrì, 1960s – 1970s) As a geometric method (Le Roux and Rouanet, 2004) Adjustment of inertia (Greenacre, 2006)
  10. 10. Why MCA Francesco Tarasconi and Vittorio Di Tomaso 10 1)It accounts for different volumes in the original variables (masses), but focuses on the shape of data (residuals) 2)Graphical method 3)Symmetric treatment of topics and emotions
  11. 11. EXAMPLES
  12. 12. Social TV Emotional Landscape Francesco Tarasconi and Vittorio Di Tomaso 12
  13. 13. X Factor’s Emotional Phases Francesco Tarasconi and Vittorio Di Tomaso 13
  14. 14. MasterChef’s Quirks Francesco Tarasconi and Vittorio Di Tomaso 14
  15. 15. X-Factor vs MasterChef Francesco Tarasconi and Vittorio Di Tomaso 15
  16. 16. Conclusions and Further Researches Francesco Tarasconi and Vittorio Di Tomaso 16 We have shown how to represent and highlight important emotional relations between topics using carefully chosen multivariate techniques. In future we would like to: add information about the authors to our analysis; study in greater detail the clouds of impressions, documents and authors.
  17. 17. We would like to thank: V. Cosenza and S. Monotti Graziadei for stimulating these researches; the ISI-CRT foundation and CELI S.R.L. for the support provided through the Lagrange Project; A. Bolioli for the essential help and supervision in the preparation of this paper. Grazie per l’attenzione! Pisa, 9/12/2014

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