Stress Detection of User in Social Networks Based on ... · Stress Detection of User in Social Networks Based on Social Interactions 1Chinta Someswara Rao , 2K V S S R Murthy, 3V

Stress Detection of User in Social Networks Based on

Social Interactions 1Chinta Someswara Rao,

2K V S S R Murthy,

3V MNSSVKR Gupta and

4R. Shiva Shankar

1Department of CSE,

S.R.K.R Engineering College,

Bhimavaram, W.G. District, A.P. India.

[email protected] 2Department of CSE,


Bhimavaram, W.G. District, A.P. India. 3Department of CSE,


Bhimavaram, W.G. District, A.P. India. 4Department of CSE,


Bhimavaram, W.G. District, A.P. India.

Abstract Mental pressure is debilitating individuals' wellbeing. It is non-

insignificant to identify pressure opportune for proactive care. With the

notoriety of web based life, individuals are accustomed to imparting their

every day exercises and associating to companions via web-based

networking media stages, making it practical to use online interpersonal

organization information for push discovery. In this paper, we find that

clients push state is firmly identified with that of his/her companions in

web based life, and we utilize a substantial scale dataset from true social

stages to efficiently think about the relationship of client's pressure states

and social associations. we dissecting the social association information,

additionally find a few interesting wonders, i.e. the quantity of social

structures of meager associations of focused on clients.

Key Words:Social network, Psychological stress, social interactions, data

International Journal of Pure and Applied MathematicsVolume 119 No. 18 2018, 225-236ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

225

mining; machine learning.

1. Introduction

For the most part, information mining (now and then called information or

learning revelation) is the way toward breaking down information from

alternate points of view and abridging it into helpful data - data that can be

utilized to build income, cuts costs, or both. Information mining programming

is one of various scientific instruments for breaking down information[1,2]. It

enables clients to break down information from a wide range of measurements

or points, arrange it, and abridge the connections distinguished. Actually,

information mining is the way toward discovering connections or examples

among many fields in substantial social databases[3,4].

2. Literature Survey

Ben Verhoeven et al., [1] chipped away at "A multilingual twitter stylometry

corpus for sex and identity profiling", which identifies identity characteristics of

writers in light of composing style. A few identity typologies exist, the Myers-

Briggs Type Indicator (MBTI) is especially famous in the non-academic

network, and numerous individuals utilize it to examine their own identity and

discuss the outcomes on the web. In this manner, a lot of self surveyed

information on MBTI are promptly accessible via web-based networking media

stages, for example, Twitter.

Golnoosh Farnadi et al., [2] investigated towards "Computational identity

acknowledgment in web based life" about an assortment of ways to deal with

naturally surmise clients' identity from their client produced content in online

life. Methodologies vary as far as the machine learning calculations and the

capabilities utilized, kind of used impression, and the internet based life

condition used to gather the information.

Quan Guo et al., [3] has created "Learning hearty uniform highlights for cross-

media social information by utilizing cross auto encoders". Cross-media

examination misuses social information with various modalities from numerous

sources all the while and synergistically to find learning and better comprehend

the world. While customary element learning techniques center around

managing single methodology information or information combined over

numerous modalities.

Novak P Kralj et al., [4] created "Estimation of emoticons" and talked about

another age of emojis, called emoticons that is progressively being utilized as a

part of versatile correspondences and online networking. In the previous two

years, more than ten billion emoticons were utilized on Twitter. Emoticons are

Unicode realistic images, utilized as shorthand to express ideas and thoughts.

The feeling examination of the emoticons enables us to make a few fascinating

inferences. The feeling dispersion of the tweets with and without emoticons is

International Journal of Pure and Applied Mathematics Special Issue

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altogether unique.

H. Lin et al., [5] made their work on "Mental pressure location from cross-

media microblog information utilizing profound inadequate neural system"

which demonstrates customary mental pressure recognition generally depends

on the dynamic individual support, which influences the discovery to work

expending, time-costing and hysteretic. With the quick improvement of

informal communities, individuals turn out to be increasingly ready to share

states of mind by means of microblog stages.

H. Lin et al., [6] has contributed on "Client level mental pressure identification

from internet based life utilizing profound neural system", which suggests that

current pressure discovery techniques for the most part depend on mental scales

or physiological gadgets, making the location entangled and expensive. In this

task, first investigate to naturally distinguish person's mental pressure by means

of web-based social networking.

Liqiang Nie et al., [7] chipped away at "Connecting the vocabulary hole

between wellbeing searchers and medicinal services information" giving

insights about the vocabulary hole between wellbeing searchers, the cross-

framework operability and the between client reusability. This paper exhibits a

novel plan to code the medicinal records by mutually using neighborhood

mining and worldwide learning approaches.

Jian Bo Yang et al., [8] contributed "Profound convolutional neural systems on

multichannel time arrangement for human movement acknowledgment". They

centers around human action acknowledgment (HAR) issue, in which inputs are

multichannel time arrangement signals gained from an arrangement of body

worn inertial sensors and yields are predefined human exercises. This technique

embraces a profound convolutional neural system (CNN) to robotize include

gaining from the crude contributions to a deliberate way. Through the profound

engineering, the educated highlights are esteemed as the more elevated amount

theoretical portrayal of low level crude time arrangement signals.

Qian Zhang and Bruno Goncalves [9] examined on "Topical contrasts between

chinese dialect twitter and sina weibo". In this investigation, They differentiate

the discourses happening on Sina Weibo and on Chinese dialect Twitter so as to

watch two unique strands of Chinese culture. A basic specially appointed

calculation is proposed to distinguish themes of Tweets and Weibos.

Xiaojun Chang et al., [10] chipped away at "Semantic idea disclosure for vast

scale zero-shot occasion discovery". They concentrated on distinguishing

complex occasions in unconstrained Internet recordings. While most existing

works depend on the plenitude of marked preparing information, They

considered a more troublesome zero-shot setting where no preparation

information is provided.


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3. System Architecture

The proposed framework includes administrator, administrator server, OSN

client and web database. In this framework, the administrator deals with every

one of the information. The OSN client registers to be a piece of social

associations. The administrator sees client points of interest, acknowledges the

information and approves the OSN client.

The OSN client looks for companions and demands them. They can see every

one of their companions.

The clients can make tweets by tweet name, tweet depiction, tweet picture and

tweet date. They can see all their made tweets and discover positive, negative

and focused on feelings on their tweets. They can see all their companion's

tweets and can retweet.

The administrator sees all companion solicitations and reactions. Administrator

can include tweet classification like positive, negative and focused on feelings.

Administrator can choose tweet classification and include tweet channel and

rundown all channels beneath.

He can list all tweets smaller scale blog with its client points of interest. He can

see positive, negative and focused on feeling tweets. He can likewise see add up

to tweets and rundown of pursuit history. The administrator additionally keeps

up the aggregate site.

We likewise utilize database for putting away and recovery motivation behind

the client subtle elements and other tweet points of interest through which

graphical investigation of the enthusiastic tweets by the clients can be found.

The system structure is shown in Fig 1.

Algorithm Explanation

Input: A series of time-varying attribute augmented network with stress states on

some of the user

nodes.

Output: Graphical Analysis of positive, negative and stressed emotion tweets.

Step 1: Admin adds emotion categories and adds filters to those categories.

Step 2: New users have to register themselves and could only login into their

accounts if admin authorizes them.

Step 3: Users send friend requests and accept other user’s requests.

Step 4: Users post tweets and their friend’s can re-tweet them.

Step 5: Users can also view their friend’s tweets.

Step 6: Admin categorizes the tweets and re-tweets into positive, negative and

stressed tweets.

Step 7: Admin provides the emotional analysis using a graph.


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Fig 1: System Architecture

Modules

Admin: In this module, the Admin has to login by using valid user name and

password. After login successful he can do some operations such as View all

End Users and Authorize, View all friend request and Response, Add Tweet

Category like Positive, Negative, Stressed, Select Tweet Category and Add

Tweet Filter and list all filters below, List all Tweets micro-blog with its user

details, View Positive (+)Emotion Tweets Emotions ,View negative (-)Emotion

Tweet Emotions ,View Stress Emotion Tweets, View total tweets and find

number of positive, negative and stressed tweets ,List of search history, Find

Admin Admin Server

OSN User

WEB

Database

Accepting user information

View user data information

Associate the admin

Process all user queries

User registration

Register and Login

View your profile

Search friends and request friends

View all your friends

Create tweet by tweet name, description, image and

date

View all your created tweets and find positive,

negative and stressed emotions

View all friend tweets and retweets by feeding your

sentiments or comments

Possibilities

View all the end users and

authorize

View all friend requests and

response

Add Tweet category like

positive, negative, stressed

Select tweet category and add

tweet filter and list all filters

below

List all tweets micro-blog with

its user details

View +, - and stress emotion

reviews and tweets

View total tweets and find

no.of +, - and stressed tweets

List of search history

po

ssibilities


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No. Of positive or negative or stressed Tweets emotion in chart

Viewing All Positive and Negative, Stress Emotions: In this module, the

admin can see all Positive and Negative Emotions posted by all users for cross

domain Micro Blogs. The Review is considered either as Positive or Negative

based on the Positive and Negative list of words which are used to find the

review as positive or negative. In this the Positive and Negative words will be

highlighted in blue color and in italics style.

View and Authorize Users: In this module, the admin can view the list of users

who all registered. In this, the admin can view the user’s details such as, user

name, email, address and admin authorizes the users.

View Chart Results: In this, the cross domain number of positive and negative

Emotions for particular post will be shown in a chart by selecting particular

Micro Blogs from a combo box.

User: In this module, there are n numbers of users are present. User should

register before doing any operations. Once user registers, their details will be

stored to the database. After registration successful, he has to login by using

authorized user name and password. Once Login is successful user will do some

operations like View your profile, Search Friends and Request, Friend. View all

Your Friends, Create Tweet by Tweet name, Tweet description, Tweet Image,

Tweet date, View all your created Tweets and find posing, Stress emotions on

your Tweets, View all your friends tweets and retweet by feeding your

sentiments or comment

Viewing All Micro Blogs Emotions and give Comment: In this, the user can

view all the Micro Blogs details, Emotions and user can comment on them by

entering their own Emotions. Each time the rank will be incremented for

particular Micro Blogs once the review is posted.

we use different tables used in our implementation. Table 1 represents for

categories, Table 2 represents for filter, Table 3 represents for friend_search,

Table 4 represents for negative, Table 5 represents for positive, Table 6

represents for req_res, Table 7 represents for retweets, Table 8 represents for

server, Table 9 represents for stressed, Table 10 represents for tweets, Table 11

represents for user structures respectively.

Table 1 categories

Field Type Constraint

si_no int(11) not null

categorie varchar2(50) Null


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Table 2 filter


categorie Text null

filter Text null

Table 3 friend_search


id int(11) not null

username Text null

keyword Text null

date Text Null

Table 4 negative


id int(11) not null

tname Text null

totaluser Text null

negative Text null

Table 5 positive


id int(11) not null

tname Text null

totaluser Text null

postive Text Null

Table 6 req_res


id int(11) not null

requestfrom Text null

requestto Text null

status Text null

dt Text null

Table 7 retweets


id int(11) not null

tname Text null

t_user Text null

t_comment Text Null

r_user Text Null

r_tweet Text Null

date Text Null


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Table 8 server


id int(11) not null

username Text not null

password Text not null

Table 9 stressed


id int(11) not null

tname Text Null

total user Text Null

stressed Text Null

Table 10 tweets


id int(11) not null

tweeter Text Null

tname Text Null

image Blob Null

description Text Null

date Text Null

rank int(11) Null

Table 11 user


id int(11) not null

username Text not null

password Text not null

email Text not null

mobile Text not null

dob Text Null

gender Text Null

address Text Null

pincode Text Null

network Text Null

image Blob Null

status Text Null

Sample registered user details are shown in Table 12.


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Table 12 Registered User Details

Usernam

e

Email Mobile Usernam

e

Email Mobile

1. manas [email protected] 978909089

0

Chiku [email protected] XXXXXXXXX

X

2. mounika [email protected]

m

934567823

1

madhavi [email protected] XXXXXXXXX

X

3. sahithi [email protected] 879067516

4

padma [email protected] XXXXXXXXX

X

4. ashok [email protected] 908453287

6

rajesh [email protected] XXXXXXXXX

X

5. satwika [email protected] 908990876

0

madhava

n

[email protected]

m

XXXXXXXXX

X

6. satish [email protected] 987687690

9

raviteja [email protected] XXXXXXXXX

X

7. tejaswini [email protected]

m

879067895

6

prakashra

j

[email protected]

m

XXXXXXXXX

X

8. Akhil [email protected] 789678567

0

Ali [email protected] XXXXXXXXX

X

9. haneesha [email protected]

m

909897989

7

Sunil [email protected] XXXXXXXXX

X

10. Sai [email protected] 897067896

4

ravibabu [email protected] XXXXXXXXX

X

4. Results

In this section, we discuss the results of the system. We define the positive,

negative and stressed emotions based on the social interactions in social

networks by the users. We make a count of the total positive, negative and

stressed emotion tweets and retweets made against that tweet. The positive

tweets data graphically represented which is shown in Fig 2. From the graph it

is observed that, If the count of the positive tweets is high, then the user is in a

positive state. This could be viewed by his positive emotional analysis.

Fig 2: Postive tweet rank graph


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The negative tweets data graphically represented which is shown in Fig 3. From

the graph it is observed that, If the count of the negative tweets is high, then the

user is in a negative state. This could be viewed by his negative emotional

analysis.

Fig 3: Negative tweet rank graph

The positive tweets data graphically represented which is shown in Fig 4. From

the graph it is observed that, If the count of the negative tweets is high, then the

user is in a negative state. This could be viewed by his negative emotional

analysis.

Fig 4: Stressed tweet rank graph

5. Conclusions

In this paper, we presented a framework for detecting social network people

stress from their yearly, monthly, weekly and daily data. In this paper we

consider negative, positive and stressed tweets and given these data as input to

our proposed approach. Our framework will calculates the correlation among

the posted data, separates the tweets by calculating their rank and this study will

helpful for future estimation of employee or person's behavior.


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References

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[2] Golnoosh Farnadi, Geetha Sitaraman, Shanu Sushmita, Fabio Celli, Michal Kosinski, David Stillwell, Sergio Davalos, Marie Francine Moens, and Martine De Cock. Computational personality recognition in social media. User Modeling and User- Adapted Interaction, pages 1–34, 2016.

[3] Quan Guo, Jia Jia, Guangyao Shen, Lei Zhang, Lianhong Cai, and Zhang Yi. Learning robust uniform features for cross-media social data by using cross auto encoders. Knowledge Based System, 102:64– 75, 2016.

[4] Novak P Kralj, J Smailovi, B Sluban, and I Mozeti. Sentiment of emojis. Plos One, 10(12), 2015

[5] H. Lin, J. Jia, Q. Guo, Y. Xue, J. Huang, L. Cai, and L. Feng. Psychological stress detection from cross-media microblog data using deep sparse neural network. In proceedings of IEEE International Conference on Multimedia & Expo, 2014.

[6] H. Lin, J. Jia, Q. Guo, Y. Xue, Q. Li, J Huang, L. Cai, and L. Feng. User-level psychological stress detection from social media using deep neural network. In Proceedings of ACM Int. Conference on Multimedia, 2014.

[7] Liqiang Nie, Yi-Liang Zhao, Mohammad Akbari, Jialie Shen, and Tat-Seng Chua. Bridging the vocabulary gap between health seekers and healthcare knowledge. Knowledge and Data Engineering, IEEE Transactions on, 27(2):396–409, 2015.

[8] Jian Bo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, and Shonali Krishnaswamy. Deep convolutional neural networks on multichannel time series for human activity recognition. In Proceedings of International Joint Conference on Artificial Intelligence, pages 3995–4001, 2015.

[9] Qian Zhang and Bruno Goncalves. Topical differences between chinese language twitter and sina weibo. Computer Science, 2015.

[10] Xiaojun Chang, Yi Yang, Alexander G Hauptmann, Eric P Xing, and Yao-Liang Yu. Semantic concept discovery for large-scale zero-shot event detection. In Proceedings of International Joint Conference on Artificial Intelligence, pages 2234–2240, 2015.


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Stress Detection of User in Social Networks Based on ... · Stress Detection of User in Social Networks Based on Social Interactions 1Chinta Someswara Rao , 2K V S S R Murthy, 3V