Personality

Apr 17, 2014Download as PPTX, PDF1 like218 views

Personality Recognition" includes automatic classification of authors' personality traits, that can be compared against gold standard annotation obtained by means of the big5 personality test

Personality Recognition
By:
Dhwanit Gupta(201001118)
Arpit Sharma(201101020)
Y.Sindhusha(201305518)
Charudatt Pachorkar(201102071)
Mentor:
Santosh K

PROBLEM STATEMENT
“Personality Recognition” includes automatic classification of authors’
personality traits, that can be compared against gold standard annotation
obtained by means of the big5 personality test.

CONTENTS
● Introduction
● Dataset
● Approach and Architecture
● Evaluation and Results
● Conclusion

INTRODUCTION
• Why personality recognition?
Recommender systems
Personalized Advertising
Opinion Marketing
Deception Detection
Social Network Analysis

INTRODUCTION
• Mapping personality of person to big-5 personality traits which includes:
 Extraversion – (sociable vs shy)
Neuroticism – (neurotic vs calm)
Agreeableness - (friendly vs uncooperative)
Conscientiousness - (organized vs careless)
Openness - (insightful vs unimaginative)

DATA SET
• Facebook dataset of 250 users of about 10000 status.
• Essay dataset of about 2400 essays

APPROACHES
• Approach-1 – Feature based approach
• Approach-2 – Trigram approach

FEATURE BASED APPROACH
Feature Extraction
Feature Vector Representation
Feature Vector Dimension Reduction
Classification(Bayesian)

FEATURE EXTRACTION
• Style based features
• Sentimental Analysis
• Total number of posts of author
• Concept Extraction
• Social networking features

• Extroverts tend to use
• Dictionary words
• 2nd person,3rd person singular
• Past tense verbs
• Neurotic users tend to
• Update their status with anger words and
• Less likely to use social interaction words

FEATURE VECTOR DIMENSION REDUCTION
• Why?
• And How?
• using Correlation Coefficient Clustering

APPROACH-2
Trigram based approach
• It is based on generating two features for each status say F1 and F2
• Where F1– represents normalized frequency of trigrams w.r.t to
current personality trait
• And F2 – represents normalized frequency of trigrams w.r.t remaining
classes
• Finally train the individual classifier using SVM for feature vector
(F1,F2)

EVALUATION
• Classifiers used
 SVM
 Bayesian
• With Dataset division as:
 70% - training and
 30% - testing

EVALUATION MEASURES
• Precision
• Recall
• F-Score

RESULTS
Personality
Trait
Accurac
y
True
Positiv
e
Rate(T
P)
Recall
False
Positive
Rate(FP
)
True
Negative
Rate(TN)
False
Negativ
e
Rate(FN
)
Precisi
on
F-
score
Trigram
Accurac
y
Extroversion 74% 0.28 0 1 0.72 1 0.44 41.17%
Openness 70% 1 1 0 0 0.695 0.82 70.58%
Neuroticism 62% 0.577 0.348 0.652 0.407 0.652 0.613 43.13%
Agreeableness 60% 0.833 0.64 0.36 0.166 0.5555 0.667 58.82%
Conscientiousne
ss
56% 1 0.9166 0.0833 0 0.532 0.695 50.98%

CONCLUSION
Results shows that style based features gives better results over
trigram approach.

REFERENCES
• http://clic.cimec.unitn.it/fabio/wcpr13/verhoeven_wcpr13.pdf
• http://clic.cimec.unitn.it/fabio/wcpr13/celliwcpr13.pdf
• http://clic.cimec.unitn.it/fabio/wcpr13/farnadi_wcpr13.pdf
• http://clic.cimec.unitn.it/fabio/wcpr13/tomlinson_wcpr13.pdf
• http://clic.cimec.unitn.it/fabio/wcpr13/markovikj_wcpr13.pdf6
• http://clic.cimec.unitn.it/fabio/wcpr13/alam
• http://clic.cimec.unitn.it/fabio/wcpr13/mohammad_wcpr13.pdf
• http://clic.cimec.unitn.it/fabio/wcpr13/appling_wcpr13.pdf

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Personality

1. Personality Recognition By: Dhwanit Gupta(201001118) Arpit Sharma(201101020) Y.Sindhusha(201305518) Charudatt Pachorkar(201102071) Mentor: Santosh K

2. PROBLEM STATEMENT “Personality Recognition” includes automatic classification of authors’ personality traits, that can be compared against gold standard annotation obtained by means of the big5 personality test.

3. CONTENTS ● Introduction ● Dataset ● Approach and Architecture ● Evaluation and Results ● Conclusion

4. INTRODUCTION • Why personality recognition? Recommender systems Personalized Advertising Opinion Marketing Deception Detection Social Network Analysis

5. INTRODUCTION • Mapping personality of person to big-5 personality traits which includes:  Extraversion – (sociable vs shy) Neuroticism – (neurotic vs calm) Agreeableness - (friendly vs uncooperative) Conscientiousness - (organized vs careless) Openness - (insightful vs unimaginative)

6. DATA SET • Facebook dataset of 250 users of about 10000 status. • Essay dataset of about 2400 essays

7. APPROACHES • Approach-1 – Feature based approach • Approach-2 – Trigram approach

8. FEATURE BASED APPROACH Feature Extraction Feature Vector Representation Feature Vector Dimension Reduction Classification(Bayesian)

9. FEATURE EXTRACTION • Style based features • Sentimental Analysis • Total number of posts of author • Concept Extraction • Social networking features

10. Why these features?

11. • Extroverts tend to use • Dictionary words • 2nd person,3rd person singular • Past tense verbs • Neurotic users tend to • Update their status with anger words and • Less likely to use social interaction words

12. • Feature Vector representation

13. FEATURE VECTOR DIMENSION REDUCTION • Why? • And How? • using Correlation Coefficient Clustering

14. APPROACH-2 Trigram based approach • It is based on generating two features for each status say F1 and F2 • Where F1– represents normalized frequency of trigrams w.r.t to current personality trait • And F2 – represents normalized frequency of trigrams w.r.t remaining classes • Finally train the individual classifier using SVM for feature vector (F1,F2)

15. EVALUATION • Classifiers used  SVM  Bayesian • With Dataset division as:  70% - training and  30% - testing

16. EVALUATION MEASURES • Precision • Recall • F-Score

17. RESULTS Personality Trait Accurac y True Positiv e Rate(T P) Recall False Positive Rate(FP ) True Negative Rate(TN) False Negativ e Rate(FN ) Precisi on F- score Trigram Accurac y Extroversion 74% 0.28 0 1 0.72 1 0.44 41.17% Openness 70% 1 1 0 0 0.695 0.82 70.58% Neuroticism 62% 0.577 0.348 0.652 0.407 0.652 0.613 43.13% Agreeableness 60% 0.833 0.64 0.36 0.166 0.5555 0.667 58.82% Conscientiousne ss 56% 1 0.9166 0.0833 0 0.532 0.695 50.98%

18. CONCLUSION Results shows that style based features gives better results over trigram approach.

19. REFERENCES • http://clic.cimec.unitn.it/fabio/wcpr13/verhoeven_wcpr13.pdf • http://clic.cimec.unitn.it/fabio/wcpr13/celliwcpr13.pdf • http://clic.cimec.unitn.it/fabio/wcpr13/farnadi_wcpr13.pdf • http://clic.cimec.unitn.it/fabio/wcpr13/tomlinson_wcpr13.pdf • http://clic.cimec.unitn.it/fabio/wcpr13/markovikj_wcpr13.pdf6 • http://clic.cimec.unitn.it/fabio/wcpr13/alam • http://clic.cimec.unitn.it/fabio/wcpr13/mohammad_wcpr13.pdf • http://clic.cimec.unitn.it/fabio/wcpr13/appling_wcpr13.pdf

20. THANK YOU

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