user behavior modelling & recommendation system based on social networks

User Behavior Modeling &

Recommendation System Based On

Social Networks

Presented by Alam Shah

Najeeb, Ahmad Taher

Hossain MD. Shakawat

American International University-Bangladesh (AIUB)

Advisor: Md. Saddam Hossain, Assistant Professor, Department of

Computer Science, American International University-Bangladesh.

Abstract

Our main approach is to suggest a person

regarding the person’s specific interests

which are anticipated based on the

person’s public data analysis. These data

can be used in further business analysis to

recommend products or services of

different companies depending on the

consumer’s personal choice.

Slide Life: Definition of Behaviour

BIG FIVE factors

Reality Demand

Applications

Scope of Implementation

Relative Works

Research Question

Research Methodology

Data Collection

Data Analysis

Results

Recommendation System

Conclusion

Behaviour is the way in which one acts orconducts oneself, especially towards others.

Psychologists believe that there are five basicdimensions of personality, often referred to asthe “Big Five” personality traits [1]. The fivebroad personality traits described by the theoryare extraversion, agreeableness, openness toexperience, conscientiousness, and neuroticism.

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Reality DemandsBig FiveDefinition of Behavior Scope of Implementation

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Introduction Related Works Research QuestionsResearch

MethodologyConclusion

Definition of Behaviour

Applications

Extraversion: refers to excitability, sociability,talkativeness, assertiveness and high amountsof emotional expressiveness towards others.

Agreeableness: refers to being helpful,cooperative and sympathetic towards others.

Conscientiousness: It concerns the way inwhich we control, regulate, and direct ourimpulses.

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Big Five Factors [1]




Applications

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Neuroticism: Is characterized by moodiness,

worry, envy, frustration, jealousy, and loneliness.

Openness to experience: It involves with active

imagination (fantasy), aesthetic sensitivity,

attentiveness to inner feelings, preference for

variety, and intellectual curiosity.

Big Five Factors (cont.)

Reality DemandsBig Five (cont.)Definition of Behavior Scope of Implementation



Applications

An employee needs a vacation and if

his/her boss is listed as friend on

OSN(online social networks) then the

employee gets the chance to apply for his

demand according to boss’s behavior

generated by the system (Neuroticism

indicates higher chances of disagree when

Agreeableness indicates higher chances of

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Reality Demands




Applications

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Reality Demands (cont.)

A business company wants to attract new

customers. If the company can know which

types of people want to buy its products

then it will be easier for the company to

reach the right customers.

Reality Demands (cont.)Big FiveDefinition of Behavior Scope of Implementation



Applications

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A company manager needs to hire an

employee, to hire the right guy for the right

job the employer might want to analyze the

employee’s behaviour.




Applications

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A political organisation wants to gain more

popularity. If the party can determine which

types of people are already its member

and which types of people are eager to join

their party then the party can recruit more

efficiently.





Applications

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To help business companies to make the right

marketing decisions by analyzing people’s

behaviour and current trends.

To help an organisation to recruit the right

person of the right job.

To help people know each other better.

To help social scientists to analyze people’s

behaviour.

Usefulness of this project

Reality Demands Big FiveDefinition of Behavior Scope of Implementation



Applications

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Applications

In this era of internet social networks are

very popular among people. Two third of

the world population spent 10% of their

time in internet in online social networks.

[2]

Scope of Implementation

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Collaborative Recommendation Location Based Social Network Big Five Modeling



Sentiment Analysis of Informal Text

People in an existing social network can

expand their social structure with the new

interdependency derived from their

locations. As location is one of the most

important components of user context,

extensive knowledge about an individual’s

interests and behaviour can be learned

from the person’s location.

Location Based Social Network [3]





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Recommendation systems can be based

on user collaboration.

Collaborative Recommendation Based

Social Network [4]





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Sentiment Intensity Analysis of Informal

texts: Sentiment analysis, also known as

opinion mining, has known considerable

interest recently.

Sentiment Intensity Analysis of Informal

Texts [5]





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It is possible to create an automated system

that can categorize users according to Big

Five personality.

1. Extraversion

2. Agreeableness

3. Conscientiousness

4. Neuroticism

5. Openness to experience

Big Five Modelling [1]



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Research Questions

How to categorize users of OSN according

to Big Five factors from their behaviours in

OSN?

Sub research Questions:

1. How OSN represent one user?

2. How can we analyze user behaviour ?

3. How to categorize user behaviour in Big

Five factors?

Data CollectionMethodology Result



Data Analysis

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Research Methodology

We follow co-relational and exploratory

methodology in this research. We use this

methodology to make relationship among

text corpus from social network with

psychological theory of personality.

Recommendation

System

USER

LIWC

Mapping

OSN(Twitter)

Twitter API

Represents

Figure: Modelling User Behaviour

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Twitter, a social network site, can be used for

sentiment analysis as it has a very large

number of short messages(tweets) created by

its users [6]. So we used Twitter to collect

users’ data. All the data collected are public

data so there are no barriers to use these

data. Using Twitter REST api 1.1, we

collected users’ public tweets, re-tweets and

followed pages in text files.

Data Collection




Data AnalysisRecommendation

System

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We did not use Facebook(another social

networking site) because most of the users

of Facebook restrict their profile

information and status updates to 'private'.

So extracting or using private data will not

be ethical. On the other hand many users

of Twitter make their profile public. So

there are no barriers to use these public

data.

Ethical Issues regarding Data Collection

Data Collection(cont.)Methodology Result




System

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We used Linguistic Inquiry and Word

Count (LIWC) which is a text analysis

software program designed by James W.

Pennebaker, Roger J. Booth, and Martha

E. Francis.

Data Analysis





System

WHY LIWC




Data Analysis(cont.)Recommendation

System

LIWC features

LIWC2007 is designed to accept written or

transcribed verbal text which has been stored as a

text or ASCII file using any of the popular

word processing software packages.

LIWC2007 reads each designated text file, one

target word at a time in a fraction of second.

32 word categories tapping psychological constructs,

7 personal concern categories, 3 paralinguistic

dimensions, and 12 punctuation categories.

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System

Table: LIWC output variable Information

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Category Example

Biological Process Eat, blood, Pain

Ingestion Dish, Eat, Pizza

Achievement Earn, Hero, Win

Insight Think, Know, Consider

Hear Hearing, Listening





System

Extraversion Openness to

Experience

Neuroticism Conscientiou

sness

Agreeableness

Social

process

Leisure Swear words Relativity Positive emotion

Family Insight Negation Motion Feel

Friends Body Negative

emotion

Space Discrepancy

Humans Ingestion Anxiety Time Tentative

Biological

process

Anger Religion Hear

Sexual Sadness Death

Achievement Sexual Money

Certainty

See

Table: LIWC categories under Big Five Factors

Results

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LIWC saved the result after reading a word

document in a .dat file.

Linear regression formula is used to sum up the

values of each categories.

f(x) = x1+x2+x3+.......+xi

Percentage formula part / whole = % /100 is used to

visualize the result in pie chart.





System

Results in a pie chart

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Openness

Extraversion

Agreeableness

Neuroticism

Conscientiousness

Data CollectionMethodology Result(cont.)




System

Figure: Recommendation System

USER

Recommendation System

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User A, B, C are followers of Age of Empires game

page. After analyzing their tweets and retweets,

machine maps the users’ behaviour and it seems

that the major part of their behaviour is extrovert.

Now after analyzing the tweets/retweets of another

user X, if the machine finds that majority of this

user’s behaviour is influenced by extroversion then

we can recommend this user game like Age of

Empires.





System

Big Five Factor Video Games Movies Music

Extraversion Strategy(Age of

Empires,

Commandos)

Political, Fantasy,

Family

Rock

Openness to

Experience

Racing(Need for

Speed)

Comedy, Sports,

Drama

Classical, New

released, Vocal

Neuroticism Shooting(Call of

Duty, Counter

Strike)

Crime, Action,

Horror

Pop, Heavy

metal

Conscientiousne

ss

Chess, Sudoku Political, History,

Conspiracy

Classical

Agreeableness Sports

Games(Fifa)

Romantic, Drama Romantic





System(cont.)

Table: Types of products under big five factor

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Conclusion To the best of our knowledge this is the first

recommendation system based on Big Five factors.In our thesis we proved that personality can bedetermined by analyzing language cues.

At this moment our system can only use textinformation, But in future our system will be able tomine data from shared links or videos. There is a bigscope for analyzing exclamatory sentences orsmileys. Our system can not understand sarcasticbehaviour at this moment, it neither can understanddouble negation.

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Thank You

References

1. Kendra Cherry. The big five personality dimensions, 2012. Accessed:2010-

09-30.

2. Nielsen Online Report. Social networks & blogs now 4th most popular

online activity, 2009.

3. Zheng, Yu. "Location-based social networks: Users." Computing with Spatial

Trajectories. Springer New York, 2011. 243-276.

4. Konstas, Ioannis, Vassilios Stathopoulos, and Joemon M. Jose. "On social

networks and collaborative recommendation." Proceedings of the 32nd

international ACM SIGIR conference on Research and development in

information retrieval. ACM, 2009.

5. Paltoglou, Georgios, et al. "Sentiment analysis of informal textual

communication in cyberspace." Proc. Engage (2010): 13-25.

6. A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and

opinion mining.,” in LREC, 2010.2 3 4 5 6 7 8 9

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user behavior modelling & recommendation system based on social networks

Social Media

business analysis

persons public data

ones public data

osn user behaviour

business sector

public relations

era of osn

hisher specific interests