Minha apresentação intitulada "Towards Author Profiling - A Methodology for Gender Expression Classification" aconteceu no evento PAPIs.io em sua edição LATAM no dia 21/06 das 12:00 às 12:30. Nela verso um pouco sobre como processamento de linguagem natural e aprendizagem de máquina, o famoso machine learning, têm sido utilizados para a criação de perfis de usuários (mesmo anônimos), principalmente em mídia social, utilizando somente texto! Vocês conseguem imaginar a importância desse tipo de técnica para análise forense de mensagens de contéudo criminoso, ou para a criação de segmentação de clientes baseado somente no que eles escrevem? De quebra ainda apresento como se dá a implementação de uma dessas novas técnicas, fruto da minha dissertação, para se descobrir expressão de gênero. #vamojunto #partiupapis #bora #papisio #machinelearning #deeplearning #datascience #bigdata #aibrasil #dtx

1. T O W A R D S A U T H O R P R O F I L I N G
A M E T H O D O L O G Y F O R G E N D E R E X P R E S S I O N C L A S S I F I C A T I O N
J o s é A h i r t o n B a t i s t a L o p e s F i l h o , M S c .

2. A D V I S O R
P r o f . D r . L e a n d r o N u n e s d e C a s t r o
P r o f . D r . R o d r i g o P a s t i
C O - A D V I S O R
L a b o r a t ó r i o d e C o m p u t a ç ã o N a t u r a l - L C o N
P r o g r a m a d e P ó s - G r a d u a ç ã o e m e n g e n h a r i a E l é t r i c a e C o m p u t a ç ã o
U n i v e r s i d a d e P r e s b i t e r i a n a M a c k e n z i e

3. AGENDA
2
UCTION
THEORETICAL REFERENCE
GENEC METHODOLOGY
PERFORMANCE EVALUATION
INTRODUCTION

4. INTRODUCTION

5. INTRODUCTION
WEB 2.0

6. MOTIVATION
A
U
TO
M
A
TIC
U
SER
CHARAC
TERIZA
TIO
N

7. MOTIVATION
A
U
TO
M
A
TIC
U
SER
CHARAC
TERIZA
TIO
N

8. MOTIVATION
A
U
TO
M
A
TIC
U
SER
CHARAC
TERIZA
TIO
N

9. MOTIVATION
A
U
TO
M
A
TIC
U
SER
CHARAC
TERIZA
TIO
N

10. Your love got me
You hit me so hard with your love.
He knocked me out, he stunned me
It came out, I went to the canvas, it was K.O.
I've always been a warrior, but first-
I saw myself helpless, heart lost the fight, yes
Goodbye drunk, single life, I want Friday
To be with you in my bed, together, coladin
Kiss me all night, sexy in the bathtub
I'm going to make you tired, I want to start
from the beginning
And look, I'll look at you
I'll tell you, I went to the canvas with your
Your love got me
You hit me so hard with your love.
He knocked me out, he stunned me
It came out, I went to the canvas, it was K.O.
I've always been a warrior, but first-
I saw myself helpless, heart lost the fight, yes
Goodbye drunk, single life, I want Friday
I saw myself helpless, heart lost the fight, yes
Goodbye drunk, single life, I want Friday
I saw myself helpless, heart lost the fight, yes
Goodbye drunk, single life, I want Friday
Your love got me
You hit me so hard with you
He knocked me out, he stun
It came out, I went to the canvas
I've always been a warrior, b
I saw myself helpless, heart lost t
Goodbye drunk, single life, I w
To be with you in my bed, togeth
Kiss me all night, sexy in the
I'm going to make you tired, I w
from the beginning
And look, I'll look at yo
I'll tell you, I went to the canvas
Your love got me
You hit me so hard with you
He knocked me out, he stun
It came out, I went to the canvas
I've always been a warrior, b
I saw myself helpless, heart lost t
Goodbye drunk, single life, I w
I saw myself helpless, heart lost t
genero
comunicação
cultura
GENDER EXPRESSION

11. ARCHITECTURE
COLLECT DATABASE PRE-PROCESSING META-ATTRIBUTES EXTRATION
META-ATTRIBUTES
SELECTION
CLASSIFICATIONRESULT

12. PRE-PROCESSING
DATABASE PRE-PROCESSING
TOKENIZATION
POS TAGGING

13. PRE-PROCESSING

14. PRE-PROCESSING
[('Como', u'KS'), ('dizem', u'V'), ('meus', u'PROADJ'), ('sobrinhos',
u'N'), ('de', u'PREP'), ('Twitter', None), (',', u','), ('rimos', None),
('litros', u'N'), ('.', u'.'), ('Embora', u'KS'), ('eu', u'PROPESS'), ('nao',
u'ADV'), ('entenda', u'V'), ('como', u'PREP'), ('se', u'PROPESS'),
('possa', u'VAUX'), ('medir', u'V'), ('o', u'ART'), ('volume', u'N'),
('espacial', u'ADJ'), ('de', u'PREP'), ('risos', u'N'), ('.', u'.'), ('Mas',
u'KC'), ('tudo', u'PROSUB'), ('bem', u'ADV')]
TWEET PRE-PROCESSING

15. META-ATTRIBUTES EXTRATION
CARACATERES E
SINTAXE
CHARACTERS AND
SYNTAX
WORDS STRUCTURE
TEXTUAL
MORPHOLOGY PSYCHO-LINGUISTICS
# TOTAL
CHARACTERS
: ; ...YULE'S K
HONORE'S R
SICHEL'S S
SIMPSON' D
ENTROPY
ORGANIZATION
OF CONTENT
EMOTIONAL,
PSYCHOLOGICAL
AND COGNITIVE
MARKERS
16 28 10 06 68
128

16. META-ATTRIBUTES EXTRATION 128
134
0.7761
0.0298
0.0
0.1716
tweet feature vector
white/Cwhite/C
dígitos/C
A-Z/C
a-z/C
C
VALUEMETA-ATTRIBUTES

17. CLASSIFICATION

18. PERFORMANCE EVALUATION

19. PERFORMANCE EVALUATION

20. PERFORMANCE EVALUATION
100 tweet
METHOD MOST SIGNIFICANT META-ATTRIBUTESTECHNICAL
CONCATENATEDTWEETS(100)
INFORMATION
GAIN

21. PERFORMANCE EVALUATION
500 tweet
METHOD TECHNICAL MOST SIGNIFICANT META-ATTRIBUTES
CONCATENATEDTWEETS(500)
INFORMATION
GAIN

22. PERFORMANCE EVALUATION
RESULTS
META-ATTRIBUTES SET
CHARACTERS AND SYNTAX
WORDS
TEXTUAL STRUCTURE
TEXTUAL MORPHOLOGY
PSYCHO-LINGUISTICS
ALL

23. CONCLUSIONS
~70%
DECISION TREE
~75%
SUPPORT VECTOR MACHINE

24. CONCLUSIONS
Forensic analysis of
anonymous messages
Targeting for
recommendation tools
E-mail makerting Construction of
user profiles

25. "Work dedicated to Guilherme Henrique Ferreira Nunes Lopes, my
brother (in memoriam), an adventurer who with his simplicity taught me
to respect the nature of things. Let us respect each one, just as the
surfer learned to respect the sea, the wave, the infinite in each one. "

26. THANK YOU
WWW.RESEARCHGATE.NET/PROFILE/JOSE_AHIRTON_LOPES_FILHO2
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COMPLETE THESES