OSN (new) ppt for optimised neural network and formation
- 1. Presentation by: Prashant Dattu Jagtap Guide HoD Prof.Lalita Randive Dr. Kavita V.Bhosale Director Dr. N. G. Patil MIT College,Aurangabad. Implementing Naive Bayes Classification to Identify Intrusive and Offensive Text on Social Media Platform
- 2. 1.Introduction Today the most popular, interactive medium to communicate, share and disseminate a considerable amount of human life information are On-line Social Networks (OSNs). Daily and continuous communications imply the exchange of several types of content, including free text, image, audio and video data.
- 3. Unwanted messages are an inherent part of today’s spamming world. User needs a stringent prevention and a good firewall against such spamming messages. The aim of the present work is to propose an automated system, called Filtered Wall (FW), able to filter unwanted messages from OSN user walls. 2.Motivation
- 4. E-commerce Social Networking sites Web Forums Discussion Panels 3.Applications:
- 5. The present work defines the construction of a system which supports content-based message filtering for Social Networking Platforms (SNP), depending on Machine Learning techniques. Proposed system has relationships with the state of the art in content-based filtering, and with the field of policy-based personalization for SNPs and, generally in web contents. 4.Literature Survey
- 6. Documents refined using content-based filtering are mostly text documents and thus content-based filtering comes nearer to text classification. Multi-label text categorization which tags messages is used by more complicated filtering systems. The set of classes considered in our experiments is Neutral; Violence; Vulgar; Offensive; Hate; Sex. 5.Content-Based filtering
- 7. Proposed system utilizes similar concept to identify the users to which a FR applies. As we are dealing with filtering of unwanted contents, one of the important factors of our system is the availability of a description for the message contents to be accomplished by the filtering mechanism 5.1 Policy-based personalization of SNP contents
- 8. Proposed method depends on 3-tier architecture which supports SNP services. 1.Goal of first layer is to deliver basic SNP functionalities. First layer is called as Social Network Manager (SNM). 2.Second layer is known as Social Network Applications (SNAs). 3.Third layer is called as Graphical User Interfaces (GUIs) which is additional layer to support some needed SNAs. 6.System Development
- 9. Architecture which supports to SNP services depends on 3-tier architecture as shown in above figure.
- 10. User collaborate with system by using GUI for the purpose of setting and managing their FRs/BLs. GUI provides the functionality of Fire Walls (FWs), on which only certified messages are displayed according to their FRs/BLs rules. The basic parts of our implemented system are Content-Based Messages Filtering (CBMF) and the Short Text Classifier (STC). 6.1 System Information
- 11. 1) After arriving into the private wall of one of the contacts in friend list, the user tries to post a message, which is intercepted by FW. 2) This abstracted data by classifier is further used by FW along with social graph and users profiles, to enforce the filtering and BL rules. 3) According to the generated outcome of step 2, either message will be published on wall or filtered by FW. 6.2 System Information
- 12. A message is available only if it is not blocked by any of the filtering rules that apply to the message creator. A filtering rule FR is a tuple (author, creatorSpec, contentSpec, action), where author is the user who specifies the rule. If user profile does not enclose a value for the attributes referred by a FR, the wall owner can make a decision whether to block or notify messages. 7.Working with Rules – 7.1Filtering Rules
- 13. The concept of Blacklist Management is used to bypass messages from unwanted peoples, irrespective of what they exactly consists of. Through BL rules, wall holder is capable to block unknown persons, peoples with which wall holder have only indirect relationships or peoples about whom wall holder have some cheap opinion. BLs is directly supervised by the system which should be capable to establish who are the users to be placed in the BL and decide when user’s retention in the BL is finished. 7.2 Blacklist
- 14. 7.2.2 Blacklisting Measures: 1) If user has been into blacklist for more times than some defined threshold, then that user will remain into blacklist unless user’s behavior is not improved. 2) Relative Frequency (RF) is used to catch bad behaviors of users. The task of RF is to find out those users whose messages always try to break down the filtering rules.
- 15. P(c|x) is the posterior probability of class (c, target) given predictor (x, attributes). P(c) is the prior probability of class. P(x|c) is the likelihood which is the probability of predictor given class. P(x) is the prior probability of predictor. 7.2.3 Naive Bayes Algorithm for Text Classifier:
- 16. 8.Experimental Analysis: Fig: a message filtered by the wall’s owner FRs
- 17. The set of classes considered in our experiments is Neutral; Violence; Vulgar; Offensive; Hate; Sex. All messages posted by users are checked against these set of classes. 1266 messages from publicly accessible groups have been selected and extracted by means of an automated procedure that removes undesired spam messages. For each message, stores the message body and the name of the group from which it originates. The messages come from the group’s web page section, where any registered user can post a new message or reply to messages already posted by other users.
- 18. The system uses Machine Learning soft classifier to implement FRs and BL to boost filtering preference. FRs should allow users to state constraints on message creators. Proposed system allows user to decide to describe BL list and to decide who has to be banned from their walls and for how long. Therefore, a user might be banned from a wall, by, at the same time, being able to post in other walls. 9.Conclusion-
- 19. By analyzing the user’s behavior in the past, learning methods applied for content-based filtering in proposed system find out the proper and relevant documents. This technique yields to restrain to user to prepare documents similar to those already seen. So, the approach is recognized as over- specialization problem.
- 20. M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari, “Content-based filtering in Social Networking Platforms,” in Proceedings of ECML/PKDD Workshop on Privacy and Security issues in Data Mining and Machine Learning (PSDML 2010), 2010. M. Chau and H. Chen, “A Machine Learning approach to web page filtering using content and structure analysis,” Decision Support Systems, vol. 44, no. 2, pp. 482–494, 2008. R. J. Mooney and L. Roy, “Content-based book recommending using learning for text categorization,” in Proceedings of the Fifth ACM Conference on Digital Libraries. New York: ACM Press, 2000, pp. 195–204. N. J. Belkin and W. B. Croft, “Information filtering and information retrieval: Two sides of the same coin?” Communications of the ACM, vol. 35, no. 12, pp. 29–38, 1992. 10.References
- 21. S. Pollock, “A rule-based message filtering system,” ACM Trans- actions on Office Information Systems, vol. 6, no. 3, pp. 232– 254, 1988. P. W. Foltz and S. T. Dumais, “Personalized information delivery: An analysis of information filtering methods,” Communications of the ACM, vol. 35, no. 12, pp. 51–60, 1992. P. E. Baclace, “Competitive agents for information filtering,” Communications of the ACM, vol. 35, no. 12, p. 50, 1992. A. Adomavicius, G.and Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transaction on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.