IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 54-59 International Journal of Research in Information Technology (IJRIT)

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ISSN 2001-5569

Rule Based Data Filtering In Social Networks Using Genetic Approach and Constrained Co Clustering S.Sanmathi#1, M.Kalimuthu*2 #Department Of Information Technology Sns College Of Technology, Vazhliyampalam, Saravanampatti,Coimbatore-35 India [email protected] *Sns College Of Technology, Vazhliyampalayam, Saravanampatti, Coimbatore-35, India. [email protected]

Abstract— In today’s online world there is a need to understand a premium way out to get better the data filtering method in social networks. By implementing the complicated data mining techniques the system deals with the undesired data filtering with text mining approaches. The system introduces the new algorithm which is named as Semantic Text Coclustering (STC) and genetic Constrained Co clustering (CCC). The algorithm deals with the two basic problems. The problems are unlabeled data clustering and semantic data filtering. The future approach uses text co clustering method to adopt both labeled and unlabeled data with high clustering performance. The system also provides a technique to effectively filter rule based contents with the use of Semantic Gap Analysis (SGA). The system also allows users make to order the filtering criteria to be applied to their walls. The system aims at developing a technique to automatically construct and optimize new text constraints. The key extractor algorithm helps to construct the rules and semantic library by extracting semantic labels. The experiment results show the process and performance comparison of the system produces superior results than previous system. Keywords—Semantic text coclustering, Semantic gap analysis, semi supervised learning, unsupervised learning, Constrained coclustering

1. INTRODUCTION On-line Social Networks (OSNs) have turn out to be a popular interactive medium to communicate, split and spread a large quantity of human life information. Daily and continuous communication implies the swap of more than a few types of content, including free text, image, audio and video data. The huge and dynamic character of these data creates the premise for the employment of web content mining strategies expected to automatically find out useful information inactive within the data and then give an dynamic hold in multifarious and sophisticated tasks involved in social networking analysis and management. A main part of social network content is constituted by short text, a notable example are the messages permanently written by OSN users on meticulous public/private areas, called in common walls. Text mining is the process of taking out of texts from a document or sentence which deals with the machine supported analysis of text

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 54-59 Text mining is defined as the non-trivial withdrawal of concealed, previously unknown, and potentially functional information from (huge quantity of) textual data. The term text mining is commonly used to denote any system that analyses large quantities of natural language text and detects lexical or linguistic usage patterns in an attempt to extract possibly of use information. The study of text mining concerns the expansion of a variety of numerical, statistical, linguistic and pattern-recognition techniques which allow automatic analysis of shapeless information as well as the drawing out of high quality and relevant data, and to make the text as a whole improved searchable. When clustering textual data, one of the most important distance procedures is manuscript likeness Since manuscript similarity is often determined by word similarity, the semantic relationships between words may affect document clustering results. Moreover, the relationships among vocabularies such as synonyms, antonyms and hyponyms, may also affect the working out of document likeness. Therefore, introduce extra knowledge on documents and words may make easy document clustering. To slot in word and document constraints, An approach called constrained information-theoretic coclustering..

2. RELATED WORK The main part of this paper is the system provided that customizable content based message filtering for OSNs, Based on ML Techniques and co-clustering techniques. Therefore, in what follows, we survey the literature in both these fields. 2.1 CONTENT-BASED FILTERING IN ON-LINE SOCIAL NETWORKS A system to filter out undesired messages from OSN walls. The structure exploits a Machine Learning soft classifier to impose customizable content-depended filtering rules. The flexibility of the system in terms of filtering options is improved trough the management of Blacklists. The early encouraging results we have obtained on the classification procedure prompt us to continue with other work that will aim to improve the eminence of classification. Additionally, to enhance our filtering rule system, with a more complicated move toward to manage those messages caught just for the patience and to decide when a user should be inserted into a Blacklists. The system can involuntarily take a decision about the messages blocked because of the lenience, on the basis of some statistical data as well as data on creator profile. To test the toughness of system against dissimilar opponent models. The growth of a GUI to make easier Blacklists and filtering rule specification is also a directions plan to investigate. 2.2 COCLUSTERING Most coclustering algorithms deal the document and word co-occurrence frequencies. The dyadic data can be modeled as a bipartite graph, and then supernatural graph theory is adopted to solve the separation problem. The co-occurrence frequencies can also be determined in co-occurrence matrices and then matrix factorizations are utilized to solve the clustering problem. The document and word co-occurrence can also be formulated as a two-sided generative model using a Bayesian interpretation. Moreover, the coclustering algorithm as an information-theoretic dividing wall, which is mathematically corresponding to the empirical joint probability distribution of two isolated random variables. Later, extended this method to a universal coclustering and matrix factorization framework. 2.3 A.SEMI-SUPERVISED CLUSTERING There are two types of semi-supervised clustering methods: semi-supervised clustering with label seeding points and semi-supervised clustering with labeled constraints. Constraint-based clustering methods often use pair wise constraints such as “must-links” and “cannot-links” to enhance unsupervised clustering algorithms. Although these constraints are also called “side-information,” most of them are built on human provided labels and the clustering methods are thus considered as semi-supervised learning. 3. IMPLEMENTATION PROCESS The planned system focus on document-level, sentence level sentiment classification or general domains in conjunction with topic detection and opinion sentiment analysis, based on the semantic label annotation techniques. An moreover genetic approach has been recommended and the proposed system finally identifies whether the semantic direction of the given text is positive, negative, or neutral. This can detect sentiment and topics at the same time with active learning pattern.

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 54-59 An effective procedure for text co clustering in STC (semantic text co clustering) for opinion and topic categorization is proposed. The objective of the future system is given that and clustering data from social sites using weakly supervised, active leaning and unsupervised learning process. An approach for semantic document clustering based on structured and sentence based clustering technique is the objective. At first the reviews and documents from the social pages are clustered in Static method using Active Learning Processing technique combine For document clustering and identifying the exact topic and opinion TOSE (Topic_Opinion_Sentiment Extraction) has been used, all documents should be preprocessed in the initial stage. The completion starts with a large set of possible extractable set of syntactic, semantic and discourse level features. The fitness function calculates the accuracy of the subjectivity classifier based on the fittest feature set identified by natural selection through the process of crossover and mutation after each generation. The subjectivity classification problem can be viewed as a summation of the subjectivity probability of the set of possible features. This is a new level of combination which includes the co clustering technique with effective and further classification technique called as genetic approaches. The implementation of genetic helps to do several iterations with the available dataset. The genetic modal performs the cross over and mutation concepts with the available dataset with the considerations of suffix and prefix based co clustering. Genetic -based TOSE methods often use pair wise constraints such as “not good” and “too bad” to enhance unsupervised clustering algorithms. Although these constraints are also called “side-information,” most of them are built on human provided labels and the clustering methods are thus considered as semi-supervised learning. 4. ALGORITHMS 4.1 PORTER STEMMER ALGORITHM The preprocessing method includes the stemming process, which eliminates needless keys. All stemming algorithms can be roughly classified as affix removing, statistical and mixed. Affix removal stemmers apply set of transformation rules to each word, trying to cut off known prefixes or suffixes. Porter stemmer utilizes suffix stripping techniques rather than prefix methods. The porter stemmer Algorithm dates from 1980. Step 1: get rid of plurals and -ed or -ing suffixes Step 2: Turns incurable y to i when there is one more vowel in the stem Step 3: Maps twice suffixes to only ones:-ization, -ational, etc. Step 4: Deals through suffixes, -full, -ness etc. Step 5: Takes off -ant, -ence, etc. Step 6: Removes a final –e The above steps represent the process and elimination of porter stemmer algorithm.. The importance of the stemmer algorithm is, it reduces the difficulties of data classification when the training data’s are insufficient. This effectively eliminates the suffix words such as ‘ed’, ‘ing’ etc., The pseudo code for the above algorithm is represented below 1.String s 2.Split String s and stored into s[] 3.for each word in s[]

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 54-59 4.if S[i].text end with “ed” 5.Remove the two keys from the word 6.Store s1[i] 7.Else if S[i].text end with “ing” 8. Remove the three keys from the word 9.Store s1[i] 10.else if ends(“s”)||ends(“ss”) 11.do step 9 4.2 G-TOSE algorithm Input: document and words sets D and V; cluster numbers Kd and Kv; co clustered constraints M and C. Initialize: document and word cluster labeles using k means. Step:1 read the initial dataset Step2: Preprocess the data using stemming algorithm, tokenizing also performed Step3: find unique T word and its frequency n Step4: find cluster C Step5: if(cluster/label found for the text T) then do step6 Step6:add to the cluster Step 7: else find semantic from data repository Sd. Find hypernym, synonym and do step 4 Step 8: start co clustering process In Gene_TOSE the new terms are preprocessed and then words are assigned to cluster one by one in recursive steps. The new words are assigned to a cluster dynamically in run time without the need of re-clustering and also with mechanical annotation of different key terms. As a result, the final step of clustering the proposed system will obtain the best evidence and provides effective topic or category of the set of words. For instance a user uploaded a document with set of positive words, the system initially finds single clustering phase and annotate the labels. Finally it perform cross verification, mutation functions to confirm the outlook into a particular cluster. 4.3 KEY EXTRACTION ALGORITHM Key Extraction Algorithm [KEA], is used to make the Keywords for auto-indexing purpose. This algorithm includes two clause:

1. Keywords, is a one word term. 2. Key Phrase, is implies a multi-word lexeme.

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 54-59 Both over terms are usually used in huge document collections. They explain the content of solo documents and supply a kind of semantic metadata that is useful for a wide variety of purposes. For that task Keyphrase Indexing is used. - Conveying keyphrases to a document is called keyphrase indexing. This task of indexing can be done in two ways:

1. complimentary Indexing 2. Indexing with Controlled Vocabularies, which one should be used, depends on the condition. 5. UNSUPERVISED CONSTRAINTS In this section, we show how to generate additional semantic constraint for clustering. Particularly, we introduce named-entity-based document constraints and Word-Net relatedness-based word constraints using the following approaches. 5.1 DOCUMENT CONSTRAINTS In practice, document constraints construct based on human comments are not easy to attain. To cope with this problem, in this work, we suggest new methods to derive “good but imperfect” constraints using information auto-matically extracted from either the content of a document or existing knowledge sources. For example, if two documents share the same people names such as “Barack Obama,” “Sarah Palin,” and “John McCain,” then both documents are possibly about US politics, thus both are likely to be in the same document cluster. Equally, if two papers share the same organization names such as “AIG,” “Lehman Brothers,” and “Merrill Lynch,” then both of them may be belong to the same document cluster about the monetary markets. Consequently, the document must-link constraints can be constructed from the correlated named entities such as person, place, and association. Specifically, if there are overlapping NEs in two documents and the number of overlapping NEs is larger than a predefined threshold, we may add a must-link to these documents. 5.2 WORD CONSTRAINTS In addition named-entity-based document constraints, it is possible to incorporate additional lexical constraints derived from existing knowledge sources to further develop clustering results. In experiment, we leverage the information in WordNet, an online lexical database, to construct word constraints. Particularly, the semantic distance of two words can be computed based on their relationships in WordNet. Since we can construct word must-links based on semantic distances, for example, we can add a word must-link if the distance between two words is less than a threshold, extra lexical information can be seamlessly incorporated into the clustering algorithm to derive better word clusters. Also, since word comprehension can be transferred to the document side during coclustering, with additional word constraints, it is possible to further improve document clustering as well. 6. CONCLUSION AND FUTURE WORK This presented a system to filter undesired messages from OSN walls. The system exploits a genetic approach based classifier to implement customizable content-dependent rule verification. The proposed system established how to in actual fact analyze and filter various response or messages using word constraints and apply them to the re-clustering process for opinion recognition with the help of genetic approach. There are more than a few guidelines for upcoming research. The study of unsupervised constraints is still beginning. This will further examine whether better text features that can be automatically derived by using natural language processing or information extraction tools. The future work may also interested in applying to other text investigation applications such as visual text summarization.

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