Code:-
JPJA2314
Abstract:-
Existing System:-
Indeed, today OSNs provide very little support to prevent 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 offriends). However, no content-based preferences are supported and therefore it is not possible to prevent undesired messages, such as political or vulgar ones, no matter of the user who posts them.</p>
Disadvantages of Existing System:-
However, no content-based preferences are supported and therefore it is not possible to prevent undesired messages, such as political or vulgar ones, no matter of the user who posts them.</li> <li>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.</li> <li>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.</li>
Proposed System:-
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 Machine Learning (ML) text categorization techniques to automatically assign with each short text message a set of categories based on its content.</p> <p style="text-align: justify;">The major efforts in building a robust short text classifier(STC) are concentrated in the extraction and selection of a set of characterizing and discriminant features. The solutions investigated in this paper are an extension of those adopted in a previous work by us from which we inherit the learning model and the elicitation procedure for generating preclassified data. The original set of features,derived from endogenous properties of short texts, is enlarged here including exogenous knowledge related to the context from which the messages originate. As far as the learning model is concerned, we confirm in the current paper the use of neural learning which is today recognized as one of the most efficient solutions in text classification. In particular, we base the overall short text classification strategy on Radial Basis Function Networks (RBFN) 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 premisefor an adequate use in OSN domains, as well as facilitatesthe experimental evaluation tasks.We insert the neural model within a hierarchical two level classification strategy. In the first level, the RBFN categorizes short messages as Neutral and Non neutral; in the second stage, Non neutral messages are classified producing gradual estimates of appropriateness to each of the considered category.Besides classification facilities, the system provides a powerful rule layer exploiting a flexible language to specify Filtering Rules (FRs), by which users can state what contents,should not be displayed on their walls. FRs can support a variety of different filtering criteria that can be combined and customized according to the user needs. More precisely,FRs exploit user profiles, user relationships as well asthe output of the ML categorization process to state the filtering criteria to be enforced. In addition, the system provides the support for user-defined Blacklists (BLs), that is, lists of users that are temporarily prevented to post any kind of messages on a user wall.</p>
Advantages of Proposed System:-
A system to automatically filter unwanted messages from OSN user walls on the basis of both message content and the message creator relationships and characteristics.</li> <li>The current paper substantially extends for what concerns both the rule layer and the classification module.</li> <li>Major differences include, a different semantics for filtering rules to better fit the considered domain, an online setup assistant (OSA) to help users in FR specification, the extension of the set of features considered in the classification process, a more deep performance evaluation study and an update of the prototype implementation to reflect the changes made to the classification techniques.</li>
Hardware Requirements:-
Software Requirements:-
- Operating System : Windows XP</li> <li>Programming Language : JAVA/J2EE</li> <li>Java Version : JDK 1.6 & above.</li> <li>DATABASE : MYSQL</li> <li>Tool : Netbeans IDE 7.2.1</li>
Cost:-
Rs 2000
Filtering Unwanted Messages in Online <br>Social Networking User walls
Code:
JPJA2314
Abstract:
Existing System:
Indeed, today OSNs provide very little support to prevent 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 offriends). However, no content-based preferences are supported and therefore it is not possible to prevent undesired messages, such as political or vulgar ones, no matter of the user who posts them.</p>
Disadvantages of Existing System:
However, no content-based preferences are supported and therefore it is not possible to prevent undesired messages, such as political or vulgar ones, no matter of the user who posts them.</li> <li>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.</li> <li>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.</li>
Proposed System:
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 Machine Learning (ML) text categorization techniques to automatically assign with each short text message a set of categories based on its content.</p> <p style="text-align: justify;">The major efforts in building a robust short text classifier(STC) are concentrated in the extraction and selection of a set of characterizing and discriminant features. The solutions investigated in this paper are an extension of those adopted in a previous work by us from which we inherit the learning model and the elicitation procedure for generating preclassified data. The original set of features,derived from endogenous properties of short texts, is enlarged here including exogenous knowledge related to the context from which the messages originate. As far as the learning model is concerned, we confirm in the current paper the use of neural learning which is today recognized as one of the most efficient solutions in text classification. In particular, we base the overall short text classification strategy on Radial Basis Function Networks (RBFN) 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 premisefor an adequate use in OSN domains, as well as facilitatesthe experimental evaluation tasks.We insert the neural model within a hierarchical two level classification strategy. In the first level, the RBFN categorizes short messages as Neutral and Non neutral; in the second stage, Non neutral messages are classified producing gradual estimates of appropriateness to each of the considered category.Besides classification facilities, the system provides a powerful rule layer exploiting a flexible language to specify Filtering Rules (FRs), by which users can state what contents,should not be displayed on their walls. FRs can support a variety of different filtering criteria that can be combined and customized according to the user needs. More precisely,FRs exploit user profiles, user relationships as well asthe output of the ML categorization process to state the filtering criteria to be enforced. In addition, the system provides the support for user-defined Blacklists (BLs), that is, lists of users that are temporarily prevented to post any kind of messages on a user wall.</p>
Advantages of Proposed System:
A system to automatically filter unwanted messages from OSN user walls on the basis of both message content and the message creator relationships and characteristics.</li> <li>The current paper substantially extends for what concerns both the rule layer and the classification module.</li> <li>Major differences include, a different semantics for filtering rules to better fit the considered domain, an online setup assistant (OSA) to help users in FR specification, the extension of the set of features considered in the classification process, a more deep performance evaluation study and an update of the prototype implementation to reflect the changes made to the classification techniques.</li>
Hardware Requirements:
Software Requirements:
- Operating System : Windows XP</li> <li>Programming Language : JAVA/J2EE</li> <li>Java Version : JDK 1.6 & above.</li> <li>DATABASE : MYSQL</li> <li>Tool : Netbeans IDE 7.2.1</li>
Cost:
Rs 2000
Additional Information
Tools Used:
Java
Cost:
₹Rs 2000
