i

IMPLEMENTING FILTERED WALL IN ONLINE

SOCIAL NETWORKING SITE

A PROJECT REPORT

Submitted by

MANASY M (211611104076)

NIVEDHITHA R (211611104092)

RANJINI PRIYA R (211611104110)

in partial fulfilment for the award of the degree

Of

BACHELOR OF ENGINEERING

IN

COMPUTER SCIENCE AND ENGINEERING

RAJALAKSHMI ENGINEERING COLLEGE

ANNA UNIVERSITY: CHENNAI 600 025

APRIL 2015

ii

ANNA UNIVERSITY: CHENNAI 600 025

BONAFIDE CERTIFICATE

Certified that this project report IMPLEMENTING FILTERED WALL IN

ONLINE SOCIAL NETWORKING SITE is the bonafide work of

MANASY.M (211611104076), NIVEDHITHA.R (211611104092),

RANJINI PRIYA.R (211611104110) who carried out the project work

under my supervision.

SIGNATURE SIGNATURE

Dr. G. SUJITHA Mr.DHANASEKARAN .S

HEAD OF THE DEPARTMENT SUPERVISOR

Professor, Assistant Professor,

Department of Computer Science and Department of Computer Science and

Engineering, Engineering,

Rajalakshmi Engineering College, Rajalakshmi Engineering College,

Thandalam, Chennai - 602105. Thandalam, Chennai - 602105.

3

ANNA UNIVERSITY: CHENNAI 600 025

This project report is submitted for viva voice examination to

be held on ____________.

1. MANASY. M (211611104076) 2. NIVEDHITHA. R (211611104092) 3. RANJINI PRIYA.R (211611104110)

INTERNAL EXAMINER EXTERNAL EXAMINER

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ACKNOWLEDGEMENT

First we thank the Almighty god for the successful completion of the project.

Our sincere thanks to our Chairman Mr. S. MEGANATHAN, B.E., F.I.E., for

his sincere endeavour in educating us in his premier institution. We would

like to express our deep gratitude to our beloved Chairperson

Dr. (Mrs.), THANGAM MEGANATHAN, Ph.D., for providing us with all

the necessary resources and other facilities towards the completion of the

project.

We also express our sincere gratitude to our college Principal,

Dr. G. THANIGAIARASU, B.E., M.Sc., Ph.D., who helped us in providing

the required facilities in completing the project. We would like to thank

Dr. G. SUJITHA, Ph.D., Head Of the Department of Computer Science and

Engineering for her encouragement for completing the project.

We would like to express our sincere appreciation and gratitude to our guide

Mr. DHANASEKARAN.S, B.Tech., M.Tech for his guidance, constant

encouragement and support. We would like to thank our Project Coordinator

Mr. S.SURESH KUMAR, M.E., (Ph.D.) and Mr.S.VINOD KUMAR,

M.Tech., for their encouragement in successful completion of this project.We

also extend our sincere thanks to all the faculty members and supporting staffs

for their direct and indirect involvement in successful completion of the project.

We express our gratitude to our parents, friends and well wishers for their

encouragement and moral support.

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ABSTRACT

The major issue in todays Online Social Network (OSNs) is to give the users

the ability to control the messages posted on their own private wall to avoid

unwanted messages being posted. Online Social Networking (OSNs) provides

less support to this requirement. We propose a system allowing OSN users to

have a direct control on the messages posted on their wall. The present work is

to experimentally evaluate an automated system called Filtered Wall, able to

filter unwanted messages from Online Social Network user wall. This is

achieved through a flexible system that allows users to customize the filtering

criteria to be applied to their own private wall.

1

vi

TABLE OF CONTENTS

CHAPTER NO TITLE PAGE NO.

ABSTRACT v

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF ABBREVIATIONS xii

1 INTRODUCTION

1.1Life Cycle Model 1

1.1.1 Advantages 1

1.2 Data Mining 2

1.3 Online Social Networking 2

2 LITERATURE SURVEY

2.1 Literature survey 5

3 SYSTEM ANALYSIS

3.1 Existing System 10

3.1.1 Disadvantages 10

3.2 Proposed System 10

vii

4 SYSTEM REQUIREMENTS

4.1 Requirement Description 12

4.1.1 Hardware requirements 12

4.1.2 Software requirements 12

4.2 Technologies used 12

4.2.1 Php 12

4.2.2 WAMP Server 13

4.2.3 MYSQL 13

5 SYSTEM DESIGN

5.1 System Architecture 15

5.2 Data flow Diagram 16

5.3 Use case Diagram 18

5.4 Class Diagram 19

5.5 Object Diagram 20

5.6 Sequence Diagram 21

5.7 Activity Diagram 22

5.8 State chart Diagram 23

5.9 Component Diagram 24

5.10 Database Design 25

viii

5.11 Database Relationship 27

6 SYSTEM IMPLEMENTATION

6.1 Module Description 28

6.1.1 Login Authentication 28

6.1.2 Registration 28

6.1.3 Profile Generation 28

6.1.4 Accept Friend Request 29

6.1.5 Send request 29

6.1.6 Post status 29

6.1.7 Filtering Text Based On Categories 29

7 TESTING

7.1Introduction 30

7.2 Types of Testing 30

7.2.1 Unit Testing 30

7.2.2 Integration Testing 31

7.2.2.1Top down Integration 31

7.2.2.2 Bottom up Integration 31

7.2.3 Functional Testing 31

7.2.4 Black box Testing 31

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7.2.5 System Testing 32

7.3 Test Cases 33

8 CONCLUSION AND FUTURE WORK 34

APPENDIX I 35

APPENDIX II 47

REFERENCES 50

x

LIST OF TABLES

NAME TITLE PAGE NO

Table 5.10.1 USER_DETAILS 25

Table 5.10.2 USER_PROFILE 25

Table 5.10.3 IMAGES_TBL 25

Table 5.10.4 FRND_REQ 25

Table 5.10.5 FRND 26

Table 5.10.6 POST 26

Table 5.10.7 BLOCKING 26

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LIST OF FIGURES

NAME TITLE PAGE NO

Fig 5.1 System Architecture 15

Fig 5.2 Dataflow Diagram 16

Fig 5.3 Use case Diagram 18

Fig 5.4 Class Diagram 19

Fig 5.5 Object Diagram 20

Fig 5.6 Sequence Diagram 21

Fig 5.7 Activity Diagram 22

Fig 5.8 State chart Diagram 23

Fig 5.9 Component Diagram 24

xii

LIST OF ABBREVIATIONS

ABBREVIATION ACRONYMS

OSN Online Social networking

FW Filtered Wall

OSA Online Setup Assistant

BL Black listing

RBFN Radial Basis Function Networks

1

CHAPTER 1

INTRODUCTION

1.1 LIFECYCLE MODEL

The life cycle model we have choosen for our project is AGILE MODEL. Agile

development model is also a type of Incremental model. Software is developed in

incremental, rapid cycles.

1.1.1 ADVANTAGES:

Customer satisfaction by rapid, continuous delivery of useful software.

People and interactions are emphasized rather than process and tools.

Working software is delivered frequently (weeks rather than months).

Face-to-face conversation is the best form of communication.

2

1.2 DATA MINING

Data mining (the analysis step of the "Knowledge Discovery and Data Mining"

process, or KDD), an interdisciplinary subfield of computer science, is the

computational process of discovering patterns in large data sets involving methods

at the intersection of artificial intelligence, machine learning, statistics,

and database systems. The overall goal of the data mining process is to extract

information from a data set and transform it into an understandable structure for

further use. Aside from the raw analysis step, it involves database and data

management aspects, data pre-processing, model and inference considerations,

interestingness metrics, complexity considerations, post-processing of discovered

structures, visualization, and online updating.

The actual data mining task is the automatic or semi-automatic analysis of large

quantities of data to extract previously unknown interesting patterns such as groups

of data records (cluster analysis), unusual records (anomaly detection) and

dependencies (association rule mining). This usually involves using database

techniques such as spatial indices. These patterns can then be seen as a kind of

summary of the input data, and may be used in further analysis.

1.3 ONLINE SOCIAL NETWORK

Online Social Networks (OSNs) are one of the popular interactive medium to

communicate, share and disseminate a considerable amount of human life

information. Daily and continuous communications imply the exchange of several

types of content including free text. They provide an active support in complex and

sophisticated tasks involved in OSN management, such as access control or

information filtering. Information has been explored for what concerns textual

3

documents and, more recently, web content. The aim of this is mainly to provide

users a classification mechanism to avoid they useless data. In OSNs, information

filtering can also be used for a different, more sensitive, purpose. This is due to the

fact that in OSNs there is possibility of posting or commenting other posts on

particular public or private areas, called in general walls. Information filtering can

therefore be used to give users the ability to automatically control the messages

written on their own walls by filtering out unwanted messages. Today OSNs

provide little support to prevent unwanted messages on user walls. There is no

content-based preferences are supported and therefore it is not possible to prevent

undesired messages such as political or vulguar ones, no matter of the user who

posts them. Information filtering systems are designed to classify a stream of

dynamically generated information dispatched asynchronously by an information

producer and present to the user those information that are likely to satisfy his or

her requirements.

The aim of the present work is therefore to propose and experimentally evaluate an

automated system, called Filtered Wall (FW), able to filter unwanted messages

from OSN user walls. We exploit text categorization techniques to automatically

assign with each short text message a set of categories based on its content. The

major efforts in building a robust short text classifier are concentrated in the

extraction and selection of a set of characterizing and discriminant features. The

original set of features, derived from endogenous properties of short texts, is

enlarged here including exogenous knowledge related to the context from which

messages originate. One of the most efficient solutions in text classification is the

use of neural learning in this model. The neural model has two level classification

strategy . In the first level, the RFBN categorizes short messages as Neural and

4

Non-neural. In the second stage, non-neural messages are classified producing

gradual estimates of appropriateness to each of the considered category.

The system also provides a powerful layer exploiting a flexible language to specify

filtering methods, by which users can state what contents should not be displayed

on their walls. This is the first proposal of a system to filter unwanted messages

from OSN user walls on the basis of both message content and the message creator

relationships and characteristics. This is mainly based on the system providing

customizable content-based message filtering for OSN.

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CHAPTER 2

LITERATURE SURVEY

2.1 LITERATURE SURVEY

1. Content-Based Book Recommending Using Learning for Text

Categorization (august 1999)

AUTHORS: Raymond J.Mooney , Loriene Roy

DESCRIPTION:

Recommender systems improve access to relevant products and information by

making personalized suggestions based on previous examples of a user's likes and

dislikes. Most existing recommender systems use social filtering methods that base

recommendations on other users' preferences. By contrast, content-based methods

use information about an item itself to make suggestions. This approach has the

advantage of being able to recommended previously unrated items to users with

unique interests and to provide explanations for its recommendations. We describe

a content-based book recommending system that utilizes information extraction

and a machine-learning algorithm for text categorization. Initial experimental

results demonstrate that this approach can produce accurate recommendations.

These experiments are based on ratings from random samplings of items and we

discuss problems with previous experiments that employ skewed samples of user-

selected examples to evaluate performance.

DRAWBACKS:

Users have to select productive strategies for selecting good examples

themselves.

Content based methods are best at recommending unpopular items to users

with unique tastes when sufficient other user data is unavailable.

6

2. Machine Learning in Automated Text Categorization (october 2001).

AUTHOR: Fabrizio Sebastiani

DESCRIPTION:

Automated categorization (or classification) of texts into predefined categories has

witnessed a booming interest in the last ten years, due to the increased availability of

documents in digital form and the ensuing need to organize them. In the research

community the dominant approach to this problem is based on machine learning

techniques: a general inductive process automatically builds a classifier by learning,

from a set of preclassified documents, the characteristics of the categories. The

advantages of this approach over the knowledge engineering approach (consisting in the

manual definition of a classifier by domain experts) are a very good effectiveness,

considerable savings in terms of expert manpower, and straightforward portability to

different domains. This survey discusses the main approaches to text categorization that

fall within the machine learning paradigm.

DRAWBACKS:

Three different problems are namely document representation, classifier construction,

and classifier evaluation.

Indispensable in many applications in which the sheer number of the documents to be

classified and the short response time required by the application make the manual

alternative implausible.

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3. Automated Learning of Decision Rules for Text Categorization (1994)

AUTHORS: Chidanand Apte, Fred Damerau, Sholom M. Weiss

DESCRIPTION:

The goal of this method is to automatically discover classification patterns that can

be used for general document categorization or personalized filtering of free text.

Previous reports indicate that human-engineered rule-based systems, requiring

many man years of developmental efforts, have been successfully built to read

documents and assign topics to them. In this paper, we show that machine

generated decision rules appear comparable to human performance, while using the

identical rule-based representation. In comparison with other machine learning

techniques, results on a key benchmark from the Reuters collection show a large

gain performance. In the context of a very high dimensional feature space, several

methodological alternatives are examined.

DRAWBACKS:

The explosive growth of electronic documents has been accompanied by an

expansion in availability of computing.It is unlikely that such information can be

managed without extensive assistance of machine.

Using dictionaries of single words does not mean that the best solution ignores

phrases and combinations of words.

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4. Combining Provenance with Trust in Social Networks for Semantic Web

Content Filtering

AUTHOR: Jennifer Golbeck

DESCRIPTION:

Social networks are a popular movement on the web. On the Semantic Web, it is

simple to make trust annotations to social relationships. In this paper, we present a

two level approach to integrating trust, provenance, and annotations in Semantic

Web systems. We describe an algorithm for inferring trust relationships using

provenance information and trust annotations in Semantic Web-based social

networks. Then, we present an application, FilmTrust, that combines the computed

trust values with the provenance of other annotations to personalize the website.

The FilmTrust system uses trust to compute personalized recommended movie

ratings and to order reviews. We believe that the results obtained with FilmTrust

illustrate the success that can be achieved using this method of combining trust and

provenance on the Semantic Web.

DRAWBACKS:

We expect that people who the user trusts highly will tend to agree with the user

more about the trustworthiness of others than people who are less trusted.

Networks are different. Depending on the subject (or lack thereof) about which

trust is being expressed, the user community, and the design of the network, the

effect of these properties of trust can vary.

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5.RCV1: A New Benchmark Collection for Text Categorization Research

(2004)

AUTHORS: David D. Lewis ,Yiming Yang,Tony G.Rose,Fan Li

DESCRIPTION:

Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually

categorized newswire stories recently made available by Reuters, Ltd. for research

purposes. Use of this data for research on text categorization requires a detailed

understanding of the real world constraints under which the data was produced.

Drawing on interviews with Reuters personnel and access to Reuters

documentation, we describe the coding policy and quality control procedures used

in producing the RCV1 data, the intended semantics of the hierarchical category

taxonomies, and the corrections necessary to remove errorful data. We refer to the

original data as RCV1-v1, and the corrected data as RCV1-v2. We benchmark

several widely used supervised learning methods on RCV1-v2, illustrating the

collections properties, suggesting new directions for research, and providing

baseline results for future studies.

DRAWBACKS:

There are gaps in the range of IDs in the original RCV1-v1, and additional gaps

(due to deleted documents) in RCV1-v2. Regrettably, the ID order does not

correspond to chronological order of the stories, even at the level of days.

The number of duplicates, foreign language documents, and other anomalies

present in RCV1 is problematic depends on the questions a researcher is using

RCV1 to study.

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CHAPTER 3

SYSTEM ANALYSIS

3.1 EXISTING SYSTEM

Existing system provides little support to prevent the unwanted messages on user

walls.For example,Facebook allows users to state who is allowed to insert

messages in their walls(i.e.,friends,friends of friends,or defined groups of

friends).No content based preferences are supported and therefore it is not possible

to prevent undesired messages,such as political or vulguar ones,no matter of the

user who posts them.This is because wall messages are constituted by short text for

which traditional classification methods have serious limitations since short texts

do not provide sufficient word occurrences.

3.1.1 DISADVANTAGES

No content-based preferences are supported and therefore it is not to prevent

undesired messages, such as political or vulguar ones,no matter of the user

who posts them.

Providing this service is not only a matter of using previously defined web

content mining techniques for a different application, rather it requires to

design ad hoc classification strategies.

This is because wall messages are constituted by short text for which

traditional classification methods have serious limitations since short text do

not provide sufficient word occurrences.

3.2 PROPOSED SYSTEM

The aim of the proposed system is therefore to propose and experimentally

evaluate an automated system, called Filtered Wall (FW), able to filter unwanted

11

messages from OSN user walls. This is the basis of both message content

characteristics.

In particular, we base the overall short text classification strategy for their proven

capabilities in acting as soft classifiers, in managing noisy data and intrinsically

vague classes. Moreover, the speed in performing the learning phase creates the

premise for an adequate use in OSN domains, as well as facilitates the

experimental evaluation tasks. Non neural messages are classified producing

gradual estimates of appropriateness to each of the considered category. Besides

classification facilities, the system provides a powerful layer exploiting a flexible

language to specify Filtering method by users can state what contents should not be

displayed on their walls. Filtering can support a variety of different filtering criteria

that can be combined and customized according to the user needs. More precisely,

Filtering methods exploit user profiles, user relationships as well as the output of

the categorization process to state the filtering criteria to be enforced.

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CHAPTER 4

SYSTEM REQUIREMENTS

4.1 REQUIREMENTS DESCRIPTION

4.1.1 HARDWARE REQUIREMENTS

Processor : Intel Core

RAM : 4 GB

Speed : 1.170GHz

Monitor : SVGA

4.1.2 SOFTWARE REQUIREMENTS

Operating system : Windows 8

Coding language : php 5.5.12

Database : MySQL 5.6.17

4.2 TECHNOLOGIES USED

4.2.1php Hypertext Processor

php is a server-side scripting language designed for web development and

also used as a general purpose programming language. It is fast, flexible and

pragmatic. php code can be simply mixed with HTML code or it can be used in

combination with templating engines and web frameworks. php code is usually

processed by a php interpreter, which is usually implemented as a web servers

native module or a Common Gateway Interface(CGI) executable. After the PHP

code is interpreted and executed, the web server sends resulting output to its client,

13

usually in form of a part of the generated web page. php interpreters became

available on most existing 32-bit and 64-bit operating systems, either by building

them from the php source code, or by using pre-built binaries. The original, only

complete and most widely used php implementation is powered by the Zend

Engine and known simply as php. php includes various free and open-source

libraries in its source distribution or uses them in resulting php binary builts. php is

fundamentally an Internet-aware system with built-in modules for accessing File

Transfer Protocol (FTP) servers and many database servers. php can be deployed

on the most web servers, many operating systems and platforms and be used with

many relational database management systems. Most web hosting providers

support php for use by their clients. Originally designed to create dynamic web

pages, php now focuses mainly on server-side scripting languages that provide

dynamic content from a web server to a client.

4.2.2WAMP SERVER

WAMP Windows, Apache, MySQL and PHP, an application

server platform. WampServer is a Windows web development environment. It

refers to a software stack for the Microsoft Windows operating system. It consists

of Apache web server, OpenSSL for SSL support, MySQL database and PHP

programming language.

4.2.3 MYSQL DATABASE

MySQL database powers the most demanding web, E-

commerce and Online Transaction Processing applications. It is a fully integrated

transaction-safe, ACID compliant database with full commit, rollback, and crash

recovery and row level locking capabilities. MySQL delivers the ease of use,

14

scalability and performance that has made MySQL the worlds most popular open

source database.

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CHAPTER 5

SYSTEM DESIGN

5.1 SYSTEM ARCHITECTURE

Fig 5.1 System Architecture for Implementing Filtered Wall in Online

Social Networking

The system architecture consists of two OSN users in which there can able to

send request or accept friend request through logging into their respective

accounts. And their can update their posts on their own wall or on friends wall, if

the post content is valid then it will be posted on the wall or if it is not valid then it

will not be posted on wall by checking with the blocked words in the dataset and it

filters the words which are not valid to post on the wall. By this users have a

control on the messages posted on their wall.

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5.2 DATA FLOW DIAGRAM

A data flow diagram(DFD) is a graphical representation of the flow of data

through an information system. It differs from the flowchart as it shows the data

flow instead of the control flow of the program. A data flow diagram can also be

used for the visualization of data processing. The DFD is designed to show how a

system is divided into smaller portions and to highlight the flow of data between

those parts.

LEVEL 0:

LEVEL 1:

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LEVEL 2:

Fig 5.2Data flow diagram of filtered wall in online social networking

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5.2 USE CASE DIAGRAM

A use case is a method used in system analysis to identify, clarify, and

organize system requirements. It shows the relationship between the user and the

different use cases in which the user is involved. This is a graphic depiction of the

interactions among the elements of a system.

Fig 5.3 Use case diagram of filtered wall in online social networking

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5.4 CLASS DIAGRAM:

A class diagram in the uml is a type of static structure diagram that describes the

structure of a system by showing the systems classes,their attributes, and the

relationships between the classes.

Fig 5.4 class diagram of filtered wall in online social networking

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5.5 OBJECT DIAGRAM

An object diagram is a diagram that shows a complete or partial view of the

structure of a modeled system at a specific time. An object diagram focuses on

some particular set of object instances and attributes, and the links between the

instances.

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Fig 5.5 object diagram of filtered wall in online social networking

5.6 SEQUENCE DIAGRAM

A sequence diagram is an interaction diagram that

shows how processes operate with one another and what is their order. It is a

construct of a message sequence chart. A sequence diagram shows object

interactions arranged in time sequence.

Fig 5.6 sequence diagram of filtered wall in online social networking

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5.7 ACTIVITY DIAGRAM

Activity diagrams are the graphical representations of workflows of stepwise

activities and actions with support for choice, iteration and concurrency. Activity

diagrams are intended to model both computational and organizational processes.

23

Fig 5.7 Activity diagram of filtered wall in online social networking

5.8 STATE CHART DIAGRAM

A state diagram is a type of diagram used in computer science and related fields to

describe the behavior of systems. State diagrams require that the system described

is composed of a finite number of states.

24

Fig 5.8 state chart diagram of filtered wall in online social networking

5.9 COMPONENT DIAGRAM

The component diagrams main purpose is to show the structural relationships

between the components of a system. A component represented implementation

items, such as files and executables.

Fig 5.9 component diagram of filtered wall in online social networking

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5.10 DATABASE TABLE DESIGN:

5.10.1 USER_DETAILS:

COLUMN TYPE CONSTRAINTS

UID Int(20) NotNull(Priamry Key)

NAME Text NotNull

EMAIL Varchar(30) NotNull

PASWD Varchar(10) NotNull

DOB Varchar(10) NotNull

GENDER Text NotNull

5.10.2 USER_PROFILE:

COLUMN TYPE CONSTRAINTS

ID Int(100) Not Null(Primary Key)

UNAME Varchar(100) Not Null

STATUS Text NotNull

SCHOOL Varchar(20) NotNull

COUNTRY Text NotNull

5.10.3 IMAGES_TBL:

COLUMN TYPE CONSTRAINTS

IMAGES_ID INT(100) NotNull(Primary Key)

UID Int(100) NotNull(Foreign Key)

IMAGES_PATH Varchar(200) NotNull

SUBMISSION_DATE Date NotNull

26

5.10.4 FRND_REQ:

COLUMN TYPE CONSTRAINTS

ID Int(10) NotNull(Primary Key)

FROM Int(10) NotNull

TO Int(10) NotNull

5.10.5 FRND:

COLUMN TYPE CONSTRAINTS

ID Int(10) NotNull(Primary Key)

USER_ONE Int(10) NotNull

USER_TWO Int(10) NotNull

5.10.6 POST:

COLUMN TYPE CONSTRAINTS

ID Int(10) NotNull(Primary Key)

PST Text NotNull

USERID Int(10) NotNull(Foreign Key)

5.10.7 BLOCKING:

COLUMN TYPE CONSTRAINTS

Blockid Int(10) NotNull(Primary Key)

Blockword Varchar(100) NotNull

27

5.11 DATABASE RELATIONSHIP :

POST:

FK

FK

USER_DETAILS:

COLUMN TYPE

ID Int(10)

PST text

UID Int(10)

COLUMN TYPE

IMAGES_ID INT(100)

UID Int(100)

IMAGES_PATH Varchar(200)

SUBMISSION_DA

TE

Date

COLUMN TYPE

UID Int(20)

NAME text

EMAIL Varchar(30)

PASWD Varchar(10)

DOB Varchar(10)

GENDER text

COLUMN TYPE

ID Int(10)

USER_ONE Int(10)

USER_TWO Int(10)

FK

FRND:

28

CHAPTER 6

SYSTEM IMPLEMENTATION

6.1 MODULE DESCRIPTION:

The system consists of seven modules namely login authentication,

registration, profile generation, send friend request, accept friend request, post

status, filtering text based on categories.

6.1.1 LOGIN AUTHENTICATION:

The process of identifying an individual, usually based on a username and

password. In security systems, authentication is distinct from authorization, which

is the process of giving individuals access to system objects based on their identity.

The login form module presents visitors with a form with username and password

fields. If the user enters valid username and password then they will be granted

access to additional resources on the website

6.1.2 REGISTRATION:

The ability to create new users. New users have to give their details. It

verifies the user request and have their own account. Having their account gives

many features, including more editing options and user preferences

6.1.3 PROFILE GENERATION:

User's profile details like profile name, display picture and status are

entered by the user which gets stored in the database. Authorized users once

logged into their profile can see their details and if they wish to change any of their

information they can edit it.

29

6.1.4 ACCEPT FRIEND REQUEST:

In this module user add new friends and view their friends and

details. Logged users can see their friend list and if they wish to add friends.

6.1.5 SEND FRIEND REQUEST:

In this module user select friend to send request. logged user view

request ,accept friend request.

6.1.6 POST STATUS:

In this module user can post any photo in public wall, and any friend of

user can post comment for that photo. User can send text messages to anyone in

their friend list.

6.1.7 FILTERING TEXT BASED ON CATEGORIES:

This module manages posting comments in the user status box. Each

unwanted message has an alert that it is an unwanted message.

30

CHAPTER 7

SYSTEM TESTING

7.1 TESTING

The purpose of testing is to discover errors. Testing is the process of trying to

discover every conceivable fault or weakness in a work product. It provides a way

to check the functionality of components, sub assemblies, assemblies and/or a

finished product. It is the process of exercising software with the intent of ensuring

that the Software system meets its requirements and user expectations and does not

fail in an unacceptable manner. There are various types of test. Each test type

addresses a specific testing requirement.

7.2 TYPES OF TESTING

7.2.1 UNIT TESTING

Unit testing involves the design of test cases that validate the internal program

logic is functioning properly, and that program input procedure valid outputs. All

decision branches and internal code flow should be validated. It is the testing of

individual software unit of the application. It is done after completion of an

individual unit before integration. This is a structural testing, that relays on

knowledge of its construction and is invasive. Unit test perform basic test at

component level and tests a specific business process, application and / or system

configuration. Unit test ensure that each unit path of a business process performs

accurately to the documented specifications, and contains clearly defined inputs

and expected results.

31

7.2.2 INTEGRATION TESTING

7.2.2.1 TOP DOWN INTEGRATION

Modules integrated by moving down the program design hierarchy. Can use depth

first or breadth first top down integration. Verifies major control and decision

points early in design process. Top-level structures are tested most. Top down

integration forced (to some extent) by some development tools in programs with

graphical user interfaces.

7.2.2.2 BOTTOM UP INTEGRATION

Begin construction and testing with atomic modules (lowest level modules).

Bottom up integration testing as its name implies begins construction and testing

with atomic modules. Because modules are integrated from the bottom up,

processing required for modules subordinate to a given level is always available

and the need for stubs is eliminated.

7.2.3 FUNCTIONAL TESTING

Functional tests provide a systematic demonstration that functions tested are

available as specified by the business and technical requirements, system

documentation and user manuals.

Organization and preparation of functional test is focused on requirements, key

functions, or special test cases. In addition, systematic coverage pertaining to

identify the business process flows: data fields, predefined process, and successive

processes must be considered for testing. Before Functional testing is complete,

additional tests are identified and the effective value of current test is determined.

32

7.2.4 BLACKBOX TESTING

Black-box testing is a method of software testing that examines the functionality of

an application without peering into its internal structures or workings. This method

of test can be applied to virtually every level of software testing: unit, integration,

system and acceptance. The goal of a black-box penetration test is to simulate an

external hacking or cyber warfare attack.

7.2.5 SYSTEM TESTING

System testing of software or hardware is testing conducted on a complete,

integrated system to evaluate the system's compliance with its specified

requirements. System testing falls within the scope of black box testing, and as

such, should require no knowledge of the inner design of the code or logic. system

testing is performed on the entire system in the context of a Functional

Requirement Specifications (FRS) and/or a System Requirement Specification

(SRS).

33

34

CHAPTER 8

CONCLUSION AND FUTURE WORK

CONCLUSION

In this work, we have presented a system to filter undesired messages from OSN

walls. The system exploits a soft classifier to enforce customizable content-

dependent filtering method. Moreover, the flexibility of the system in terms of

filtering options is enhanced through the management of BLs.

FUTURE WORK

This work is the first step of a wider project .The early encouraging results we

have obtained on the classification procedure prompt us to continue with other

work that will aim to improve the quality of classification. In particular, future

plans contemplate a deeper investigation on two interdependent tasks. The first

concerns the extraction and/or selection of contextual features that have been

shown to have a high discriminative power. The second task involves the learning

phase. Since the underlying domain is dynamically changing, the collection of pre-

classified data may not be representative in the longer term. The present batch

learning strategy, based on the preliminary collection of the entire set of labeled

data from experts, allowed an accurate experimental evaluation but needs to be

evolved to include new operational requirements. In future work, we plan to

address this problem by investigating the use of on-line learning paradigms able to

include label feedbacks from users. Additionally, we plan to enhance our system

with a more sophisticated approach to decide when a user should be inserted into a

BL.

35

APPENDIX I

CODING

MODULE 1 LOGIN AUUTHENTICATION MODULE

Home

36

$tbl_name="images_tbl"; // Table name

$con=mysqli_connect($host, $username, $password,$db_name)or die("cannot

connect");

//echo "CONNECTED";

//echo "SELECTED";

$res=mysqli_query($con,"SELECT * FROM `user_details` WHERE

`PASWD`='$psd'" );

$row=mysqli_fetch_array($res);

$a=$row["UID"];

//echo $a;

$b=mysqli_query($con,"SELECT * FROM `images_tbl` WHERE

`images_id`=$a");

$rows=mysqli_fetch_array($b);

?>

LIVES IN

37

Wall

Edit

Profile

Friends

Request

Members

Profile

SHARE POINT

38

Home

RecentlyPosted

Logout

WELCOME

Recent Posts:

39

$host="localhost"; // Host name

$username="root"; // Mysql username

$password=""; // Mysql password

$db_name="sharepoint"; // Database name

$currentid=$_SESSION['MY_ID'];

$con=mysqli_connect($host, $username, $password,$db_name)or die("cannot

connect");

$quer=mysqli_query($con,"SELECT * FROM `post` ORDER BY `Id` DESC");

while($run_quer=mysqli_fetch_array($quer))

{

$txt=$run_quer["Pst"];

$_SESSION['msg']=$txt;

$usrid=$run_quer["Userid"];

if($currentid!=$usrid )

{

$query=mysqli_query($con,"SELECT `user_one`, `user_two` FROM `frnd`

WHERE `user_one`='$currentid' OR `user_two`='$currentid' ");

while($run_frnd=mysqli_fetch_array($query))

{

$user_one=$run_frnd['user_one'];

$user_two=$run_frnd['user_two'];

if($user_one==$currentid)

{

40

$user=$user_two;

}

else

{

$user=$user_one;

}

if($usrid==$user)

{

$querry=mysqli_query($con,"SELECT * FROM user_details WHERE

UID='$user'");

$run=mysqli_fetch_array($querry);

$uname=$run["NAME"];

$res=mysqli_query($con,"select * from images_tbl WHERE images_id='$user' " );

$runres=mysqli_fetch_array($res);

$pth=$runres["images_path"];?>

41

?>

MODULE 2: POST STATUS MODULE

42

$removeword1 = array();

$query = "Select * from `blocking`";

$result = mysqli_query($con,$query);

while($removeword = mysqli_fetch_array($result))

{

array_push($removeword1, $removeword['Blockword']);

//print_r($removeword1);

}

$removearrlen=count($removeword1);

for($x=0;$x

43

$stopval=0;

for($x=0;$x

44

{

echo "great";

$query=mysqli_query($con,"INSERT INTO `post` VALUES

('','$messages','$currentid')");

header('location:postfrndwall1.php');

}

?

MODULE 3:FILTERING TEXT BASED ON CATEGORIES

45

$query = "Select * from `blocking`";

$result = mysqli_query($con,$query);

while($removeword = mysqli_fetch_array($result))

{

array_push($removeword1, $removeword['Blockword']);

//print_r($removeword1);

}

$removearrlen=count($removeword1);

for($x=0;$x

46

if($removeword1[$x]==$chkarr[$y])

{

$chkval+=1;

}

else

{ $stopval+=1;

}

}

}

if ($chkval>0)

{

header('location:dispmsg.php');

}

else

{

echo "great";

$query=mysqli_query($con,"INSERT INTO `post` VALUES

('','$messages','$currentid')");

header('location:postfrndwall1.php');

}?>

47

APPENDIX II

SNAPSHOTS

Result of post status module

Snapshot ofTable based on categorization of filtered words

48

Snapshot of user profile

Snapshot of table containing user details

49

Snapshot of posting on friends wall

Snapshot of alert message due to filtering

50

REFERENCES

[1] R.J. Mooney and L. Roy, "Content-Based Book Recommending Using

Learning for Text Categorization," Proc. Fifth ACM Conf. Digital Libraries, pp.

195-204, 2000.

[2] F. Sebastiani, "Machine Learning in Automated Text Categorization," ACM

Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.

[3] C. Apte, F. Damerau, S.M. Weiss, D. Sholom, and M. Weiss, "Automated

Learning of Decision Rules for Text Categorization," Trans. Information Systems,

vol. 12, no. 3, pp. 233-251, 1994.

[4] J. Golbeck, "Combining Provenance with Trust in Social Networks for

Semantic Web Content Filtering," Proc. Int'l Conf. Provenance and Annotation of

Data, L. Moreau and I. Foster, eds., pp. 101-108, 2006.

[5] D.D. Lewis, Y. Yang, T.G. Rose, and F. Li, "Rcv1: A New Benchmark

Collection for Text Categorization Research," J. Machine Learning Research, vol.

5, pp. 361-397, 2004.

REFERENCE WEBSITES:

www.w3schools.com

www.php.net

http://www.w3schools.com/http://www.php.net/