Advances in Engineering Research (AER), volume 142 International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 2018) A Survey: Data Mining Techniques for Social Media Analysis 1Elangovan D, 2Dr.Subedha V, 3Sathishkumar R, 4 Ambeth kumar V D 1Research scholar, Department of Computer Science Engineering, Sathyabama University, India 2Professor, Department of Computer Science Engineering, Panimalar Institute of Technology, Chennai, India 3Assistant Professor, 4Associate Professor, Department of Computer Science Engineering, Panimalar Engineering College, Chennai, India [email protected],[email protected], [email protected],[email protected] Abstract—Data mining is the extraction of present information databases. The overall objective of the data mining technique from high volume of data sets, it’s a modern technology. The is to extract information from a huge data set and transform it main intention of the mining is to extract the information from a into a comprehensible structure for more use. The different large no of data set and convert it into a reasonable structure for data Mining techniques are further use. The social media websites like Facebook, twitter, instagram enclosed the billions of unrefined raw data. The I. Characterization. various techniques in data mining process after analyzing the II. Classification. raw data, new information can be obtained. Since this data is III. Regression. active and unstructured, conventional data mining techniques IV. Association. may not be suitable. This survey paper mainly focuses on various V. Clustering. data mining techniques used and challenges that arise while using VI. Change Detection. it. The survey of various work done in the field of social network analysis mainly focuses on future trends in research. VII. Deviation Detection. VIII. Link Analysis. Index Terms— Data Mining, Social media, Social network IX. Sequential Pattern Mining. Analysis, Web Mining. Social network finds its application in several business activities like Co-innovation, Customer service, General I. INTRODUCTION promoting, increasing spoken promoting, marketing research, ata mining is a powerful tool which will facilitate to seek plan generation and new development, publicity, worker D out hidden patterns and various relationship between the communication and reputation management. data. Data processing discovers hidden facts from massive II. LITERATURE SURVEY ALGORITHM S.NO AUTHOR PAPERS TITLE ADVANTAGES/LIMITATIONS / TOOLS . For various group of users, dissimilar interaction patterns that are not Identifying User similar can be experiential. Clustering 1 Marcelo Maia, et.al Behavior In Online . Characterize and identify the user Algorithm Social Networks profiles in online social networks. Display more suitable advertisements based on user behavior Using maximum group and sub grouping criteria social network Mohamad Al- Analysis Of Social structure are clustered. Cleavage Ant 2 Fayoumi, Souyma Network Using Cleaver Colony Metaphor Imposing performance on test run Banerjee, et.al Ant Colony Metaphor using the standard group sequence data particularly for the clustering of the instance named as Keller 6. Copyright © 2018, the Authors. Published by Atlantis Press. 109 This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Engineering Research (AER), volume 142 To perform analysis on public data Massive Social from a micro blogging network like David Ediger Karl Network Analysis: Graph Ct Snag 3 twitter. Jiang, et.al Mining Twitter For Algorithm Social Good focus of analysts more on a much smaller data subset. Identification Of User Pattern In Social Decision Tree 4 A.SemanBozkur et.al most precise results Networks By Algorithm Datamining Techniques SENTIWORDNET relative improvement of 19.48% for 3.0: An Enhanced StefaoBaccianella SENTIWORD- the ranking by positivity and a 5 Lexical Resource For et.al NET 3.0 relative improvement of 21.96% Sentiment Analyses Of for the ranking by negativity Option Mining Twitter As A Corpus Opinion Mining Large data set 6 Alexander Pak et.al For Sentiment Analyses And Sentiment Improved needed increasing the And Potation Mining Analysis amount of the training data. Functional Analysis Of RojalinaPriyadershini Artificial Neural Back Propagation Highly effective tool for classification. 7 et.al Network For Dataset Analysis Combination of trail, learned Classification while using the web mining for social Analyses Of Social Medias for analysis, data sampling Networks Using The Web 8 E.Raju, Sravanthi is a huge issue. TechniqueMove Web miningTechnique Mining Filtering the data is difficult. Communities Overlapping. Sentiment detection used in the Sentiment Analyses different applications such as G.Vinodhini, Sentiment 9 And Opinion Mining A identify and classified reviews, Rm.Chandrasekaran Classification Survey summarize the review and other real time methods and applications . Churn Analyses Of Decision Tree Analyze the potential mix of users. Xi Ling, Wenjing Online Social Network Classification 10 To process huge total of data in a Yin, et.al Uses Using Mining And K Means timely manner , decision tree-based Technique Algorithm and a k- means algorithms are used. A Survey On Social Social Network Link analysis and deep and dark 11 Rupam Some Network And Future Models Statically networking Is recent trends in social Trends And For Analysis networks Paridhi Jain, Identifying Users Identify the spammers and nemo while 12 PonnurangamKumara Across Multiple Social Finding Nemo using this algorithm guru, et.al Networks More efficient in information finding process Comparison Of Web Neha Mehata, Manta Conventional And Fuzzy clustering are suitable for 13 documentClusteri Kathuria et.al Fuzzy Clustering handling the issues related to ng Techniques A Survey understandability of patterns, incomplete/noisy data, mixed media in rank and human 110 Advances in Engineering Research (AER), volume 142 communication, and can provide approximate data Neuro Fuzzy Approach Noro Fuzzy To Data Clustering A 14 FarhatAroochi Clustering automatically generate the patterns Framework For Analysis Analyses Naïve Bayesian Atomic Classification Mita , K.Dalal, Model Artificial it’s an domain dependent 15 Of Unstructured Blog Mukesh et.al Neutral Network Structure Text Unstructured blog text classification Model A Survey On Using Datamining Techniques with solid basis in graph theory , 16 G.Nandi,A.Das Graph Theory For Online Social analysis of data is done Network Analyses Semi supervised To identify opinionated sentences Learning Based beginning unstructured consumer Mita K Dalal, OptimizedOpinionSum Semi Supervised 17 reviews with great success and Mukesh et.al marization And Approach categorize their orientation with Classification For acceptable accuracy Online Product Review New robotic mechanisms are needed to help in drilling from downward to Critical Review On Sentiment top micro posts. 18 Asad Bukhari, et.al Sentiment Analyses Analysis Technique Technique Improves the sentiment analysis classifiers that are able to enhance and analyze the user sentiments By combining social network analysis Empirical Analysis with content mining using Technique For Data Amalgam approach would be more 19 S.G.S Fernando Mining Technique For Markov Model functional . Social Network The statistical methods like Markova Websites models can be adopted to resolve the temporal behavior of web data. Identify and Verdict communities in social networks structure, penetrating patterns in social Critical Analyses Of Sanjeev Dhawan, Web mining networks and investigative 20 Social Networks With Kulvinder Sing, et.al Technique overlapping communities. Web Data Mining online social networking websites like online photo albums, comments and blogs. Sentimental analysis includes Emotion Mining Sentiment managing customer dealings, Sanjeev Dhawan, 21 Technique In Social Analysis emotions of human ,information Kulvinder Singh, et.al Networking Sites Technique retrieval, natural text-to-speech system, social and literary analysis. A Study Of Data Knowledge Based Focus to find global structural patterns. 22 M.Vedanayaki Mining And Social Network Analysis Network Analysis tedious to collect data 111 Advances in Engineering Research (AER), volume 142 Focus and analyze the extracted Sentiment MdAnsarulHaque, Sentiment Analyses By opinions from posted in social 23 Analysis TamjidRahaman Using Fuzzy Logic networks like facebook and twitter Technique etc.. A Survey Of Mariam Adedoyin- Datamining Technique Conducted on social network analysis 24 Tram Olewe, et.al For Social Network using the tram technique. Analyses A Survey And Comparison Of Sentiment using the sentimental analysis Anais Collomb, 25 Sentiment Analyses Analysis techniques , models target a simple CrinaCostea, et.al Methods For Technique global classification. Reputation Evaluation Neuro and fuzzy logic approaches Clustering In Defaming Neuro And Fuzzy 26 Meenu Sharma helps to improve the clustering A Breve Review Logic Approaches review Soft computing methodologies- solve Soft Computing Y.K. Soft Computing data mining problems. 27 Technique And Its MathurAbhayaNand Techniques Impact In Data Mining Discover the no pattern in the large databases. Esmaeil Datamining Technique Dynamic behavior of data. 28 FakhimiGheshlagh For Web Mining : A Web Mining et.al Review Hybrid approach The Application Of Zahra