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© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)

IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 463

ANALYZING AND IDENTIFYING SOCIAL

NETWORK MENTAL DISORDERS VIA ONLINE

SOCIAL MEDIA MINING

Dr.S.G.Balakrishnan 1 AJAZ AHMAD HURRAH2

1 ASSOCIATE PROFESSOR, 2 PG STUDENT

COMPUTER SCIENCE AND ENGINEERING

MAHENDRA ENGINEERING COLLEGE (AUTONOMOUS), TAMILNADU, INDIA

ABSTRACT

The rapid growth of social networking has led to problematic usage. Today’s world solely depends on

Social media and due to excessive use, number of mental disorders like Cyber-Relationship Addiction,

Information Overload and Net Compulsion has been observed in online users. Symptoms of these mental

disorders are usually detected passively today, which results in delayed clinical intervention. As detecting these

SNMDs is challenging through users online activity. We propose a deep learning framework that exploits features

analyzed from the Social media data mainly images to identify potential SNMDs by classifying the emotion

features and expressions like happy, angry, sad, normal, surprise disgust etc., as positive and negative and

predicting the mental disorder accordingly. Image processing, Gabor filters with Convolution Neural Networks is

used for detection and prediction of expressions of face images. The proposed is expected to provide promising

results for identifying online social network users with potential Social Network Mental Disorders based on

expression of online user.

Keywords : SNMD & Social Edia Mining

1. INTRODUCTION

With the growth and popularity of social networking and messaging apps, online social networks (OSNs)

have become part and parcel of people’s lives. Most of the research done on social media network mining mostly

concentrates on discovering the knowledge behind the data for improving user’s life. Even though OSNs have

expanded peoples capability in increasing social contact but they have actually decreased face-to-face personal

interactions in real world due to this new terms like Phubbing and Nomophobia have been coined to define

people who are addicted to using social networking apps.

Research has been already done in the past to explore data mining techniques to detect various Social

Network Mental Disorders like:

1) CYBER-RELATIONSHIP (CR) ADDICTION, which includes the addiction to social networking, checking

and messaging to the point where social relationships to virtual and online friends become more important than

real-life ones with friends and families;

2) NET COMPULSION (NC), which includes compulsive online social gaming or gambling, often resulting in

financial and job-related problems; and

3) INFORMATION OVERLOAD (IO), which includes addictive surfing of user status and news feeds, leading

to lower work productivity and fewer social interactions with families and friends offline.

In the existing research feature analysis is performed on large data sets by feeding the data of users to

machine learning framework namely Social Network Mental Disorders Detection to identify the various mental

disorders. Multimedia data like images where not considered for this analysis. In this work we will be analyzing

online images of users and perform emotions analysis using image processing and using Gabor filters and

Convolutional Neural Networks (CNN) to predict whether person is suffering from social disorder as we will be

able to determine if person is happy, hurt, sad, scared, surprised, furious etc.

http://www.ijrar.org/



2. SOFTWARE TESTING INTRODUCTION

Software testing is a process used to help identify the correctness, completeness and quality of developed

computer software. Software testing is the process used to measure the quality of developed software .Testing is

the process of executing a program with the intent of finding errors. Software testing is often referred to as

verification & validation.

Explanation for SDLC & STLC

SDLC: The software development life cycle (SDLC) is a conceptual model used in project management

that describes the stages involved in an information system development project, from an initial feasibility study

through maintenance of the completed application.

2.1 PHASES OF SOFTWARE DEVELOPMENT:

REQUIREMENT ANALYSIS

The requirements of a desired software product are extracted. Based the business scenario the SRS (Software

Requirement Specification) document is prepared in this phase.

Design Plans are laid out concerning the physical construction, hardware, operating systems, programming,

communications, and security issues for the software. Design phase is concerned with making sure the software

system will meet the requirements of the product.

THERE ARE 2 STAGES IN DESIGN,

HLD – High Level Design

LLD – Low Level Design

HLD – gives the architecture of the software product to be developed and is done by architects and senior

developers.

LLD – done by senior developers. It describes how each and every feature in the product should work and how

every component should work. Here, only the design will be there and not the code.

TESTING Testing is evaluating the software to check for the user requirements. Here the software is evaluated with intent of

finding defects.

MAINTENANCE once the new system is up and running for a while, it should be exhaustively evaluated. Maintenance must be

kept up rigorously at all times. Users of the system should be kept up-to-date concerning the latest modifications

and procedures.

2.2 SDLC MODELS

WATER FALL MODEL

It will be executing one by one of the SDLC process. The design Starts after completing the requirements

analysis coding begins after design. It is a traditional model It is a sequential design process, often used in SDLC,

in which the progress is seen as flowing steadily downwards ( like a waterfall ), through the different phases.

PROTOTYPE MODEL

Developed from the sample after getting good feedback from the customer. This is the Valuable mechanism for

gaining better understanding of the customer needs.

RAPID APPLICATION DEVELOPMENT MODEL (RAD):

This mechanism will develop from already existing one .If the new requirement is matching in already existing

requirement, will develop from that.

SPIRAL MODEL:

This mechanism is updating the application version by version. All the SDLC process will update version by

version.




V-MODELV: V model is a process where the development and testing phases can do parallel. For every development phase

there is a testing phase. Development phases are called as verification whereas testing phases are called as

validation

STLC (SOFTWARE TESTING LIFE CYCLE): Testing itself has many phases i.e. is called as STLC. STLC is part of SDLC and includes following:

2.3 TEST PLAN

It is a document which describes the testing environment, purpose, scope, objectives, test strategy, schedules,

mile stones, testing tool, roles and responsibilities, risks, training, staffing and who is going to test the application, what type of tests should be performed and how it will track the defects.

TEST DEVELOPMENT

Preparing test cases, test data, Preparing test procedure, Preparing test scenario, Writing test script

TEST EXECUTION

In this phase we execute the documents those are prepared in test development phase

ANALYZE RESULT:

Once executed documents will get results either pass or fail. we need to analyze the results during this phase.

DEFECT TRACKING:

Whenever we get defect on the application we need to prepare the bug report file and forwards to Test Team Lead

and Dev Team. The Dev Team will fix the bug. Again we have to test the application. This cycle repeats till we

get the software without defects.

TYPES OF TESTING:

Following are the types of testing:

WHITE BOX TESTING

White box testing as the name suggests gives the internal view of the software. This type of testing is also known

as structural testing or glass box testing as well, as the interest lies in what lies inside the box.

BLACK BOX TESTING

It’s also called as behavioral testing. It focuses on the functional requirements of the software. Testing either

functional or non-functional without reference to the internal structure of the component or system is called black

box testing.

GREY BOX TESTING

Grey box testing is the combination n of black box and white box testing. Intention of this testing is to find out

defects related to bad design or bad implementation of the system.

COMPONENT TESTING

Instatement checking is the primary degree of effective checking and is primary duty of designers and after that

of the testing engineers. component checking is done following the normal check outcomes are satisfied else

contrasts are logical/adequate.

INTEGRATION TESTING

All modules that do application are tried. Indulge checking the communication of minimum 2 segments produces

outcomes that fulfill practical prerequisite.


http://www.buzzle.com/editorials/4-10-2005-68350.asp



3.1 SYSTEM-TESTING

To test the total framework as far as usefulness and non-usefulness. It is black box testing, performed by the

Test Team, and toward the beginning of the framework testing the total framework is designed in a controlled

domain. To check the absolute system to the extent helpfulness and non-value. It is discovery trying,

implemented by the check Team, and around the start of the structure testing the all-out system is planned in a

controlled space.

3.2 FUNCTIONAL TESTING

The dynamic associations from all of the pages from unequivocal territory under test. Test each and

every inside association. Test associations jumping on comparative pages. Check for the default estimations of fields. Wrong contributions to the fields in the structures.

3.3 ALPHA-TESTING

Alpha-testing is last attempting before the item is released to the general populace. This testing is aimed

at the architect site and in a supervised circumstance by the last customer of the item.

3.4 BETA-TESTING

The beta-test is aimed in any event one customer areas by the end customer of the item. The beta-test is

driven at any rate one customer areas by the last customer of the item.

3.5 COMPONENT TESTING CASES

Presentation testing is the chief degree of dynamic checking and is first the commitment of originators

and subsequently that of the test engineers. Unit testing is performed after the typical test results are met or

differentiations are sensible/attractive.

A result is the last after effect of exercises or events conveyed abstractly or quantitatively. Execution

examination is an operational assessment, is a ton of fundamental quantitative association between the

introduction sums.

4. PROJECT METHODOLOGY

It will cover the details explanation of methodology that is being used to make this project complete and

working well. Many methodology or findings from this field mainly generated into journal for others to take

advantages and improve as upcoming studies. The method is use to achieve the objective of the project that will

accomplish a perfect result. In order to evaluate this project, the methodology based on System Development Life

Cycle (SDLC), generally three major step, which is planning, implementing and analysis.

Figure 1: Software Development Life Cycle




Figure 2: Steps of Methodology

4.1 LANNING:

To identify all the information and requirement such as hardware and software, planning must be done in the

proper manner. The planning phase has two main elements namely data collection and the requirements of

hardware and software.

DATA COLLECTION:

Machine learning needs two things to work, data (lots of it) and models. When acquiring the data, be sure

to have enough features (aspect of data that can help for a prediction, like the surface of the house to predict its

price) populated to train correctly your learning model. In general, the more data you have the better so make to

come with enough rows. The primary data collected from the online sources remains in the raw form of

statements, digits and qualitative terms. The raw data contains error, omissions and inconsistencies. It requires

corrections after careful scrutinizing the completed questionnaires. The following steps are involved in the

processing of primary data. A huge volume of raw data collected through field survey needs to be grouped for

similar details of individual responses.

Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other

words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for

the analysis. Therefore, certain steps are executed to convert the data into a small clean data set. This technique is

performed before the execution of Iterative Analysis. The set of steps is known as Data Preprocessing. It includes

-

Data Cleaning

Data Integration

Data Transformation

Data Reduction

Data Preprocessing is necessary because of the presence of unformatted real-world data. Mostly real-

world data is composed of -

Inaccurate data (missing data) - There are many reasons for missing data such as data is not continuously

collected, a mistake in data entry, technical problems with biometrics and much more.

The presence of noisy data (erroneous data and outliers) - The reasons for the existence of noisy data

could be a technological problem of gadget that gathers data, a human mistake during data entry and much more.




Inconsistent data - The presence of inconsistencies are due to the reasons such that existence of

duplication within data, human data entry, containing mistakes in codes or names, i.e., violation of data

constraints and much more.

In this final phase, we will test our classification model on our prepared image dataset and also measure

the performance on our dataset. To evaluate the performance of our created classification and make it comparable

to current approaches, we use accuracy to measure the effectiveness of classifiers.

After model building, knowing the power of model prediction on a new instance, is very important issue.

Once a predictive model is developed using the historical data, one would be curious as to how the model will

perform on the data that it has not seen during the model building process. One might even try multiple model types for the same prediction problem, and then, would like to know which model is the one to use for the real-

world decision making situation, simply by comparing them on their prediction performance (e.g., accuracy). To

measure the performance of a predictor, there are commonly used performance metrics, such as accuracy, recall

etc. First, the most commonly used performance metrics will be described, and then some famous estimation

methodologies are explained and compared to each other. "Performance Metrics for Predictive Modeling In

classification problems, the primary source of performance measurements is a coincidence matrix (classification

matrix or a contingency table)”. Above figure shows a coincidence matrix for a two-class classification problem.

The equations of the most commonly used metrics that can be calculated from the coincidence matrix are also

given in Fig 12.

Figure 1: Confusion Matrix

Figure 4: Confusion Formula

As being seen in above figure, the numbers along the diagonal from upper-left to lower-right represent

the correct decisions made, and the numbers outside this diagonal represent the errors. "The true positive rate

(also called hit rate or recall) of a classifier is estimated by dividing the correctly classified positives (the true

positive count) by the total positive count. The false positive rate (also called a false alarm rate) of the classifier is

estimated by dividing the incorrectly classified negatives (the false negative count) by the total negatives. The

overall accuracy of a classifier is estimated by dividing the total correctly classified positives and negatives by the

total number of samples.




5. LITERATURE SURVEY

Face detection is a computer technology that determines the location and size of human face in arbitrary

(digital) image. The facial features are detected and any other objects like trees, buildings and bodies etc., are

ignored from the digital image. It can be regarded as a ‗specific case of object-class detection, where the task is

finding the location and sizes of all objects in an image that belong to a given class. Face detection, can be

regarded as a more ‗general case of face localization. In face localization, the task is to find the locations and

sizes of a known number of faces (usually one). Basically there are two types of approaches to detect facial part

in the given image i.e. feature base and image base approach. Feature base approach tries to extract features of the

image and match it against the knowledge of the face features. While image base approach tries to get best match

between training and testing images.

Figure 5: Detection Method

6. FEATURE BASE APPROCH:

Acive shape models focus on complex non-rigid features like actual physical and higher level appearance

of features Means that Active Shape Models (ASMs) are aimed at automatically locating landmark points that

define the shape of any statistically modelled object in an image. When of facial features such as the eyes, lips,

nose, mouth and eyebrows. The training stage of an ASM involves the building of a statistical

a) Facial model from a training set containing images with manually annotated landmarks.

ASMs is classified into three groups i.e. snakes, PDM, Deformable templates

b) 1.1) Snakes: The first type uses a generic active contour called snakes, first introduced by Kass et al. in

1987 Snakes are used to identify head boundaries [8,9,10,11,12]. In order to achieve the task, a snake is first

initialized at the proximity around a head boundary. It then locks onto nearby edges and subsequently assume the

shape of the head. The evolution of a snake is achieved by minimizing an energy function, Esnake (analogy with

physical systems), denoted as Esnake = Einternal + EExternal Where Einternal and EExternal are internal and

external energy functions. Internal energy is the part that depends on the intrinsic properties of the snake and

defines its natural evolution. The typical natural evolution in snakes is shrinking or expanding. The external

energy counteracts the internal energy and enables the contours to deviate from the natural evolution and

eventually assume the shape of nearby features—the head boundary at a state of equilibria. Two main

consideration for forming snakes i.e. selection of energy terms and energy minimization. Elastic energy is used

commonly as internal energy. Internal energy is vary with the distance between control points on the snake,

through which we get contour an elastic-band characteristic that causes it to shrink or expand. On other side

external energy relay on image features. Energy minimization process is done by optimization techniques such as

the steepest gradient descent. Which needs highest computations. Huang and Chen and Lam and Yan both

employ fast iteration methods by greedy algorithms. Snakes have some demerits like contour often becomes




trapped onto false image features and another one is that snakes are not suitable in extracting non convex

features.

6.1 DEFORMABLE TEMPLATES:

Deformable templates were then introduced by Yuille et al. to take into account the a priori of facial

features and to better the performance of snakes. Locating a facial feature boundary is not an easy task because

the local evidence of facial edges is difficult to organize into a sensible global entity using generic contours. The

low brightness contrast around some of these features also makes the edge detection process. Yuille et al. took the

concept of snakes a step further by incorporating global information of the eye to improve the reliability of the

extraction process.

Deformable templates approaches are developed to solve this problem. Deformation is based on local valley,

edge, peak, and brightness .Other than face boundary, salient feature (eyes, nose, mouth and eyebrows)

extraction is a great challenge of face recognition.E = Ev + Ee + Ep + Ei + Einternal ; where Ev , Ee , Ep , Ei ,

Einternal are external energy due to valley, edges, peak and image brightness and internal energy

6.2 (POINT DISTRIBUTION MODEL):

Independently of computerized image analysis, and before ASMs were developed, researchers developed

statistical models of shape. The idea is that once you represent shapes as vectors, you can apply standard

statistical methods to them just like any other multivariate object. These models learn allowable constellations of

shape points from training example sand use principal components to build what is called a Point Distribution

Model. These have been used in diverse ways, for example for categorizing Iron Age broaches. Ideal Point

Distribution Models can only deform in ways that are characteristic of the object. Cootes and his colleagues were

seeking models which do exactly that so if a beard, say, covers the chin, the shape model can \override the image"

to approximate the position of the chin under the beard. It was therefore natural (but perhaps only in retrospect) to

adopt Point Distribution Models. This synthesis of ideas from image processing and statistical shape modelling

led to the Active Shape Model. The first parametric statistical shape model for image analysis based on principal

components of inter-landmark distances was presented by Cootes and Taylor in. On this approach, Cootes,

Taylor, and their colleagues, then released a series of papers that cumulated in what we call the classical Active

Shape Model.

6.3 LOW LEVEL ANALYSIS:

Based on low level visual features like color, intensity, edges, motion etc., Skin Color Base Color is

avital feature of human faces. Using skin-color as a feature for tracking a face has several advantages. Color

processing is much faster than processing other facial features. Under certain lighting conditions, color is

orientation invariant. This property makes motion estimation much easier because only a translation model is

needed for motion estimation. Tracking human faces using color as a feature has several problems like the color

representation of a face obtained by a camera is influenced by many factors (ambient light, object movement,

etc.,




.)

Figure 2: face detection

Majorly three different face detection algorithms are available based on RGB, YCbCr, and HIS color

space models. In the implementation of the algorithms there are three main steps viz.

Classify the skin region in the color space,

Apply threshold to mask the skin region and

Draw bounding box to extract the face image.

Crowley and Coutaz suggested simplest skin color algorithms for detecting skin pixels.

The perceived human color varies as a function of the relative direction to the illumination.

The pixels for skin region can be detected using a normalized color histogram, and can be normalized for

changes in intensity on dividing by luminance. Converted an [R, G, B] vector is converted into an [r, g] vector of

normalized color which provides a fast means of skin detection. This algorithm fails when there are some more

skin region like legs, arms, etc., Cahi and Ngan [6] suggested skin color classification algorithm with YCbCr

color space. Research found that pixels belonging to skin region having similar Cb and Cr values. So that the

thresholds be chosen as [Cr1, Cr2] and [Cb1, Cb2], a pixel is classified to have skin tone if the values [Cr, Cb]

fall within the thresholds. The skin color distribution gives the face portion in the color image. This algorithm is

also having the constraint that the image should be having only face as the skin region. Kjeldson and Kender

defined a color predicate in HSV color space to separate skin regions from background. Skin color classification

inHSI color space is the same as YCbCr color space but here the responsible values are hue (H) and saturation

(S). Similar to above the threshold be chosen as [H1, S1] and [H2, S2], and a pixel is classified to have skin tone

if the values [H,S] fall within the threshold and this distribution gives the localized face image. Similar to above

two algorithm this algorithm is also having the same constraint.

6.4 MOTION BASE:

When use of video sequence is available, motion information can be used to locate moving objects.

Moving silhouettes like face and body parts can be extracted by simply thresholding accumulated frame

differences. Besides face regions, facial feature scan be located by frame differences.

6.5 GRAY SCALE BASE:

Gray information within a face can also be treat as important features. Facial features such as eyebrows,

pupils, and lips appear generally darker than their surrounding facial regions. Various recent feature extraction

algorithms search for local gray minima within segmented facial regions. In these algorithms, the input images

are first enhanced by contrast-stretching and gray-scale morphological routines to improve the quality of local

dark patches and thereby make detection easier. The extraction of dark patches is achieved by low-level gray-

scale thresholding. Based method and consist three levels. Yang and huang presented new approach i.e. faces




gray scale behavior in pyramid (mosaic) images. This system utilizes hierarchical Face location consist three

levels. Higher two level based on mosaic images at different resolution. In the lower level, edge detection method

is proposed. Moreover this algorithms gives fine response in complex background where size of the face is

unknown

6.6 EDGE BASE

Face detection based on edges was introduced by Sakai et al. This work was based on analyzing line

drawings of the faces from photographs, aiming to locate facial features. Than later Craw et al. proposed a

hierarchical framework based on Sakai et al.‘s work to trace a human head outline. Then after remarkable works

were carried out by many researchers in this specific area. Method suggested by Anila and Devarajan was very simple and fast. They proposed frame work which consist three stepsi.e. Initially the images are enhanced by

applying median filter for noise removal and histogram equalization for contrast adjustment. In the second step

the edge image is constructed from the enhanced image by applying sobel operator. Then a novel edge tracking

algorithm is applied to extract the sub windows from the enhanced image based on edges. Further they used Back

propagation Neural Network (BPN) algorithm to classify the sub-window as either face or non-face.

6.7 FEATURE SEARCHING

VIOLA JONES METHOD:

Paul Viola and Michael Jones presented an approach for object detection which minimizes computation

time while achieving high detection accuracy. Paul Viola and Michael Jones [5] proposed a fast and robust

method for face detection which is 15 times quicker than any technique at the time of release with 95% accuracy

at around 17 fps. The technique relies on the use of simple Haar-like features that are evaluated quickly through

the use of a new image representation. Based on the concept of an ―Integral Image‖ it generates a large set of

features and uses the boosting algorithm AdaBoost to reduce the over complete set and the introduction of a

degenerative tree of the boosted classifiers provides for robust and fast interferences. The detector is applied in a

scanning fashion and used on gray-scale images, the scanned window that is applied can also be scaled, as well as

the features evaluated.

6.8 GABOR FEATURE METHOD

Sharif et al proposed an Elastic Bunch Graph Map (EBGM) algorithm that successfully implements face

detection using Gabor filters. The proposed system applies 40 different Gabor filters on an image. As a result of

which 40 images with different angles and orientation are received. Next, maximum intensity points in each

filtered image are calculated and mark them as fiducial points. The system reduces these points in accordance to

distance between them. The next step is calculating the distances between the reduced points using distance

formula. At last, the distances are compared with database. If match occurs, it means that the faces in the image

are detected.

6.9 CONSTELLATION METHOD All methods discussed so far are able to track faces but still some issue like locating faces of various

poses in complex background is truly difficult. To reduce this difficulty investigator form a group of facial

features in face-like constellations using more robust modelling approaches such as statistical analysis. Various

types of face constellations have been proposed by Burl et al. They establish use of statistical shape theory on the

features detected from a multiscale Gaussian derivative filter. Huang et al. also apply a Gaussian filter for pre-

processing in a framework based on image feature analysis. Image Base Approach.

6.10 LINEAR SUB SPACE METHOD

EIGEN FACES METHOD:

An early example of employing Eigen vectors in face recognition was done by Kohonen in which a

simple neural network is demonstrated to perform face recognition for aligned and normalized face images. Kirby

and Sirovich suggested that images of faces can be linearly encoded using a modest number of basis images. The

idea is arguably proposed first by Pearson in 1901 and then by HOTELLING in 1933 .Given a collection of n by

m pixel training. Images represented as a vector of size m X n, basis vectors spanning an optimal subspace are

determined such that the mean square error between the projection of the training images onto this subspace and

the original images is minimized. They call the set of optimal basis vectors Eigen pictures since these are simply

the Eigen vectors of the covariance matrix computed from the vectored face images in the training set.


https://www.rroij.com/open-access/face-detection-approaches-a-survey-.php?aid=46929#42416637

https://www.rroij.com/open-access/face-detection-approaches-a-survey-.php?aid=46929#42416637



Experiments with a set of 100 images show that a face image of 91 X 50 pixels can be effectively encoded using

only50 Eigen pictures.

6.11 STATISTICAL APPROCH

SUPPORT VECTOR MACHINE (SVM):

SVMs were first introduced Osuna et al. for face detection. SVMs work as a new paradigm to train

polynomial function, neural networks, or radial basis function (RBF) classifiers. SVMs works on induction

principle, called structural risk minimization, which targets to minimize an upper bound on the expected

generalization error. An SVM classifier is a linear classifier where the separating hyper plane is chosen to

minimize the expected classification error of the unseen test patterns. In Osunaet al. developed an efficient

method to train an SVM for large scale problems and applied it to face detection. Based on two test sets of

10,000,000 test patterns of 19 X 19 pixels, their system has slightly lower error rates and runs approximately30

times faster than the system by Sung and Poggio [16] . SVMs have also been used to detect faces and pedestrians

in the wavelet domain.

7. EXISTING SYSTEM

With the growth and popularity of social networking and messaging apps, online social networks (OSNs)

have become part and parcel of people’s lives. Most of the research done on social media network mining mostly

concentrates on discovering the knowledge behind the data for improving user’s life. Even though OSNs have

expanded peoples capability in increasing social contact but they have actually decreased face-to-face personal

interactions in real world due to this new terms like Phubbing and Nomophobia have been coined to define

people who are addicted to using social networking apps. Research has been already done in the past to explore

data mining techniques to detect various Social Network Mental Disorders like:

1) CYBER-RELATIONSHIP (CR) ADDICTION, which includes the addiction to social networking,

checking and messaging to the point where social relationships to virtual and online friends become more

important than real-life ones with friends and families;

2) NET COMPULSION (NC), which includes compulsive online social gaming or gambling, often

resulting in financial and job-related problems; and

3) INFORMATION OVERLOAD (IO), which includes addictive surfing of user status and news feeds,

leading to lower work productivity and fewer social interactions with families and friends offline.

In the existing research feature analysis is performed on large data sets by feeding the data of users to

machine learning framework namely Social Network Mental Disorders Detection to identify the various mental

disorders. Multimedia data like images where not considered for this analysis.

In this work we will be analyzing online images of users and perform emotions analysis using image

processing through Facial Expression Recognition (FER) and using Gabor filters and Convolutional Neural

Networks (CNN) to predict whether person is suffering from social disorder.

In conventional FER system, the developed algorithms work on the constrained database. In the

unconstrained environment, the efficiency of existing algorithms is limited due to certain issues during image

acquisition. This study presents a detailed study on FER techniques, classifiers and datasets used for analyzing

the efficacy of the recognition techniques.

8. PROPOSED SYSTEM

The most important form of non-verbal communications is facial emotions of a person. We proposed a

deep learning approach to detect a person’s face emotions in OSN images by studying their facial features like

surprise, fear, disgust, anger, happiness, and sadness using the Gabor filters and Convolutional neural network by

studying their facial features. The system extracts features from the OSN images and then classifies these features

and accordingly determines mental state of Social Network User (SNU). We will be developing social media like

application (in python) wherein we can upload images (emotion based dataset) which will be fed to image

processing system for training purpose for feature extraction and classification. The system will be trained with

features of depression. Then the emotion features of these faces will be extracted for prediction of depression.

Based on the level of depression features the user will be predicted for particular state of emotion like anger fear,

disgust, happy etc.




9. CONCLUSION

In this project we will try to automatically identify various Social Network Mental Disorders by

analyzing the OSN images using techniques of deep learning and Facial Expression Recognition to extract the

emotions of images and classify them as features. The proposed framework uses Gabor filter and Convolutional

Neural Network and is expected to extract sub features and determine the various types of emotions of online

users. Based on the facial expressions system will be able to determine whether the online user is in good or bad

mood. Also multiple screenshots recorded from the facial expressions will determine whether person is suffering

from Social Media Mental Disorder. As for the next step, we plan to increase dataset from multimedia, used for

training the Neural Network and extract features using technique like computer vision.

10. REFERENCES

[1] Adelson, E. H., and Bergen, J. R. (1986) The Extraction of Spatio-Temporal Energy

[2] Human and Machine Vision, Proceedings of Workshop on Motion: Representation and Analysis (pp. 151-

155) Charleston, SC;

[3] AAFPRS(1997). A newsletter from the American Academy of Facial Plastic and Reconstructive Surgery.

Third Quarter 1997, Vol. 11, No. 3. Page 3.

[4] Baron, R. J. (1981). Mechanisms of human facial recognition. International Journal of Man Machine

Studies, 15:137-178

[5] Beymer, D. and Poggio, T. (1995) Face Recognition From One Example View, A.I. Memo No. 1536,

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