With the coming of the World Wide Web and the rise of web-based business applications and informal organizations, associations over the web create a lot of information on a daily basis. It is becoming more complex and critical task to retrieve exact information from web expected by its users. In the recent times, the Web has extended its noteworthiness to the point of transforming into the point of convergence of our propelled lives. The search engine as an apparatus to explore the web must get the coveted outcomes for any given query. The greater part of the search engines can't totally fulfill user’s necessities and the outcomes are regularly inaccurate and irrelevant. knowledge of ontology and history is not much personalization in the existing techniques. To conquer these issues, data mining systems must be connected to the web and one advanced powerful concept is web-page recommendation which is becoming more powerful now a day. In this paper, the design of a fuzzy logic classifier algorithm is defined as a search problem in the solution space where every node represents a rule set, membership function, and the particular framework behaviour. Therefore, the hybrid optimization algorithm is applied to search for an optimal location of this solution space which hopefully represents the near optimal rule set and membership function. In this article, we reviewed various techniques proposed by different researchers for web page personalization and proposed a novel approach for finding optimal solutions to search the relevant information..

Abstract:People share knowledge, experiences and thoughts with the world by using Social Media like blogs, forums, wikis, review sites, social networks, tweets and so on. This has changed the manner in which people communicate and influence social, political and economic behavior of other people in the Web.This work mainly focus on different sentiment analysis techniques a comparative study of different sentiment analysis techniques and a proposed model that uses sentiment analysis on twitter data and user behavior prediction Proposed System mainly rely on Twitter Data.Performing sentiment analysis on twitter data and predicting the behavior of the tweets and thus the user who post those tweets. By analyzing tweets on certain groups the behavior of those groups can also be identified. Social networking websites are considered as major sources of opinions and views of the public on the prevalent social issues at a given point in time. Websites like the Twitter reflect the public views through its millions of messages posted by its users world wide,.By conducting survey these data set required for training data set is being created.and by using these training set twitter commands are analysed and thus the behavior of each tweets are extracted.Based on these tweets if any malicious activities are going on they can be detected and banned.

The Internet presents large amount of useful information which is usually formatted for its users, which makes it hard to extract relevant data from diverse sources. Therefore, there is a significant need of robust, flexible Information Extraction (IE) systems that transform the web pages into program friendly structures such as a relational database will become essential. IE produces structured data ready for post processing. Roadrunner will be used to extract information from template web pages. In this paper, we present novel algorithm for extracting templates from a large number of web documents which are generated from heterogeneous templates. The proposed system focuses on information extraction from heterogeneous web pages. We cluster the web documents based on the common template structures so that the template for each cluster is extracted simultaneously. The resultant clusters will be given as input to the Roadrunner system.

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