Social Network Leverage Search

Social networks are at an all time high, nowadays. They make the world a smaller place to live in. People can stay in touch with friends and can make new friends on these social networks which traditionally were not possible without internet service. The possibilities provided by social networks enable vast and immediate contact. People tend to spend lot of time on the social networks like Facebook, LinkedIn and Twitter peeping into their friend‟s accounts and trying to stay connected with the world.However, recently people have started closing their accounts on these famous social networks after having been irritated with the large amount of data that floods these networks. Although there are many problems associated with these social networks like: privacy issues, identity fraud, information overload, etc.; the problem that bothers people the most is that of information overload.This project provides a solution to the information overload problem by filtering all the user‟s friend‟s posts on the basis of user‟s likes without explicitly asking the user to specify their likes. The project analyzes the user\u27s posts to find out their likes, and then returns the filtered posts to them from their friends on Facebook, Twitter and LinkedIn.Thus, this project attempts to remove noise from the huge amount of data on these social networks

Ranking-Constrained Keyword Sequence Extraction from Web Documents

Given a large volume of Web documents, we consider problem of finding the shortest keyword sequences for each of the documents such that a keyword sequence can be rendered to a given search engine, then the corresponding Web document can be identified and is ranked at the first place within the results. We call this system as an Inverse Search Engine (ISE). Whenever a shortest keyword sequence is found for a given Web document, the corresponding document can be returned as the first document by the given search engine. The resulting keyword sequence is search-engine dependent. The ISE therefore can be used as a tool to manage Web content in terms of the extracted shortest keyword sequences. In this way, a traditional keyword extraction process is constrained by the document ranking method adopted by a search engine. The significance is that the whole Web-searchable documents on the World Wide Web can then be partitioned according to their keyword phrases. This paper discusses the design and implementation of the proposed ISE. Four evaluation measures are proposed and are used to show the effectiveness and e±ciency of our approach. The experiment results set up a test benchmark for further researches