The document proposes predicting personality types by analyzing social media using machine learning and natural language processing. It involves taking tweets from a user's profile and breaking them into words using Stanford Tokenizer. First order predicate logic is used to create relations between words, and fuzzy logic is used to conclude the personality type based on the analysis. Machine learning algorithms like SVM, RVM, random forest, naive bayes, and decision trees can be used for classification or regression tasks.

1. • RABI JALAN
• AJAY TOMAR
• DR. NIMISH KUMAR
ICCS-2017
PREDICTING PERSONALITY TYPES
BY ANALYZING SOCIAL MEDIA

2. •Predicting Personality Types of Users / Customers,
using their Social Media and other interactions,
using the big the "big five" parameters.
•Identifying User Intent, based on the interactions,
using Machine Learning (Semantic Parsing).
PROBLEM STATEMENT

3. A predefined number of tweets from a user’s profile will be
taken and will be broken up into words by using Stanford
Tokenizer.
Then we use First Order Predicate Logic to create relations
between specific words (to know the activity being done in the
sentences).
Using Learning method
To conclude our process we use Fuzzy logic .
STEPS OF PROBLEM
SOLUTION

4. •Natural Language Processing (NLP) refers to
AI method of communicating with an
intelligent systems using a natural language
such as English.
•Processing of Natural Language is required
when you want an intelligent system like robot
to perform as per your instructions, when you
want to hear decision from a dialogue based
clinical expert system etc.
NATURAL LANGUAGE
PROCESSING(NLP)

5. •This can be done using Stanford Tokenizer, which is
a renowned tool capable of tokenizing text at the
rate of 1,000,000 tokens per second.
•It also removes all the common problems such as
the tool’s inability to know when periods are not the
end of a sentence. It is not available independently
but is present in Stanford parser.
STANFORD TOKENIZER

6. STANFORD PARSER (ONLINE
MODE)

7. • Representing information about the world in a form that a computer system can utilize
to solve complex tasks. –
• Incorporates findings from psychology[citation needed] about how humans solve
problems and represent knowledge in order to design formalisms that will make
complex systems easier to design and build.
• First Order Predicate Logic (Fopl):
• First-order logic is symbolized reasoning which means that each sentence, or
statement, is broken down into a subject and a predicate.
• First-order logic (FOL) models the world in terms of:-
Objects, which are things with individual identities
Properties of objects that distinguish them from other objects
 Relations that hold among sets of objects
 Functions, which are a subset of relations where there is only one value for any
KNOWLEDGE REPRESENTATION

8. SYNTAX:-
• Statements are broken down into a predicate using following FOPL provides :-
• FOL Provides:-
• Variable symbols
• E.g., x, y, foo
• Predicates: likes(john, apples)
• Connectives
• Same as in PL: not (¬), and (ˇ), or implies (->), if and only if (biconditional <->)
• Quantifiers
• Universal or (Ax)
• Existential or (Ex)
SYNTAX

9. • Propositional logic is a weak language:-
• Propositional logic has limited expressive power.
• Hard to identify “individuals” (e.g., Mary, 3).
• Can’t directly talk about properties of individuals or relations between individuals.
• Generalizations, patterns, regularities can’t easily be represented.
• First-Order Logic (abbreviated FOL or FOPC) is expressive enough to concisely.
• represent this kind of information FOL adds relations, variables, and quantifiers.
WHY FOPL OVER REPOSITIONAL
LOGIC

10. TRANSLATING ENGLISH TO FOPL :-
• Every gardener likes the sun.
• Ax gardener(x) -> likes(x,Sun)
• All purple mushrooms are poisonous.
• Ax (mushroom(x) ^ purple(x) -> poisonous(x)
• “On Mondays and Wednesdays I go to John’s house for dinner”
• AX((is Mon(X) V is wed(X) -> eat_meal(me,house Of(John),X)
• Every rose has a thorn
• AX(rose(X) -> EY.(has(X,Y)^ thorn(Y)
TRANSLATING ENGLISH TO
FOPL

11. Prediction of personality traits can be performed in two ways
:
• By classification task, aim is to identify individuals with
particularly high or low values of a trait according to some
predetermined cut-off.
• By regression task, aim is to predict an individual’s score for
each of the eight personality traits based on their Twitter
usage.
MACHINE LEARNING

12. • Support Vector Machine (SVM) using sequential minimal optimization
(SMO) and a polynomial kernel.
OR
• Relevance Vector Machine (RVM)
• Random Forest, an ensemble method that combines multiple
decision trees.
• J48, an implementation of the C4.5 decision tree algorithm.
• Naïve Bayes (NB) classifier
OR
MACHINE LEARNING ALGO

13. Least mean squares (LMS) algorithms are a class
of adaptive method used to mimic a desired one by
finding the parametric coefficients that relate to
producing the least mean square of the error signal
(difference between the desired and the actual result)
A simple, correlation can conduct on the self-reported
Dark Triad and Big Five personality scores and values
obtained through analysing Twitter profile and
language data.
LEAST MEAN SQUARE(LMS)

14. •Point be similar as spearman’s correlation given in
pdf.
Narcissistic traits were also significantly positively
correlated with words associated with sex ,
increased urge to fulfil basic needs, possibly as a
reaction to not having had these basic needs
satisfactorily fulfilled earlier in life, or may be a result
of the narcissistic need for triumph and domination.
POINTS ON PERSONALITY

15.  Machiavellian traits were significantly positively
correlated with swear words, anger, negemo, use of
negative and hostile language.
 Psychopathic traits were significantly positively
correlated with swear words and negative emotions.

16. • To conclude our process we use Fuzzy logic i.e. an
approach to computing based on "degrees of truth" rather
than the usual "true or false" (1 or 0).
• Fuzzy logic seems closer to the way our brains work. We
aggregate data and form a number of partial truths which
we aggregate further into higher truths which in turn, when
certain thresholds are exceeded, cause certain further
results such as motor reaction. The sum of all weightages is
used to put the user in one of the specified categories.
FUZZY LOGIC

17. i. Stanford Parser
ii. LIWC 2007
iii. WEKA 3
iv.Statistical Analysis software as per required
v. Twitter (for primary input)
TECHNOLOGY STACK (MAY
USE)

18. • Take Input from
social site
Break input into
tokens using
tokenizer
Use FOPL to relate
tokens
Comparing using FUZZY
LOGIC & Conclude
Personality
Calculate weightage
of input sentence
Learning using LMS
method
FLOW DIAGRAM OF PROPOSED
SOL.

19. THANK YOU