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

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