A Review Paper on Social Network Analysis through Data Mining Techniques

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 9, Issue 10, October 2019)

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A Review Paper on Social Network Analysis through Data

Mining Techniques

Swati Namdev

1

, Dr. Sunil Phulre

2 1_{Research Scholar, LNCT University, Bhopal}

2_{LNCT University, Bhopal}

Abstract-- Data Mining is that the extraction of projected information from large data sets cab be a pleasant innovative technology. The goal of the data mining method is to extract information from an information set and work on it into an obvious structure for additional use. Websites contain several unprocessed information. By analyzing this information new knowledge is gained.

In this paper we tend to discuss concerning data mining techniques. During this paper a review of the works drained the sector of social network analysis and additionally concentrates on the longer term trends in analysis on social network analysis.

Keywords--Social Networks, Data Mining Techniques, Association Rule, Big Data

I. INTRODUCTION

Data Mining is a powerful tool that can help to find patterns and relationships within our data. Data mining discovers hidden information from large databases. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Social networks can be used in many activities like General Marketing, Marketing Research, Idea Generation, New Product development, increasing word-of-mouth marketing, Co-innovation, Customer Service, Public relations, Employee communication and in Reputation management.

There are various data mining techniques:

1)Tracking Patterns or Sequential Pattern

This data mining technique helps to discover or identify similar patterns or trends in transaction data for certain period.

2)Classifications

This analysis is used to retrieve important and relevant information about data and metadata. This data mining method helps to classify data in different classes.

3)Association

This data mining technique helps to find the association between two or more items. It discovers a hidden pattern in the data set.

4)Outlier detection

This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. Outer detection is also called Outlier Analysis or Outlier mining.

5)Clustering

Clustering analysis is a data mining technique to identify data that are like each other. This process helps to understand the differences and similarities between the data.

6)Regression

Regression analysis is the data mining method of identifying and analyzing the relationship between variables. It is used to identify the likelihood of a specific variable, given the presence of other variables.

7)Prediction

Prediction has used a combination of the other data mining techniques like trends, sequential patterns, clustering, classification, etc. It analyzes past events or instances in a right sequence for predicting a future event.

8)Decision Trees

A decision tree is one of the most commonly used data mining techniques because its model is easy to understand for users. In decision tree technique, the root of the decision tree is a simple question or condition that has multiple answers. Each answer then leads to a set of questions or conditions that help us determine the data so that we can make the final decision based on it.

9)KNN (K-Nearest Neighbor)

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II. LITERATURE REVIEW

S.

No. Author's Name Paper Title Techniques Findings Year References

1 Alexander Pak,

Patrick Paroubek

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

Opinion Mining and Sentiment Analysis

When the dataset is large enough, the improvement may be not achieved by only increasing the size of the training data

2010 R1

2 E. Raju

K. Sravanthi

Analysis of Social Network using the Techniques of Web Mining

Web Mining Techniques- Clustering, Association Rule Mining

Data Sampling is a big issue when using web mining for social networks analysis.

It becomes a difficult task to select suitable samples representative of the real social networks.

Other challenges include finding communities in social networks, finding patterns in social networks and analysing overlapping communities.

How to apply the web mining techniques to some real on-line social networking websites, such as blogs and on-line photo albums.

2012 R2

3

Hemant Kumar Soni Sanjiv Sharma Pankaj K. Mishra

Association Rule Mining: A Data Profiling and Prospective Approach

Association Rule Mining

there is a need to shift the paradigm form single objective to multi-objective association rule mining

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4

Heling Jiang An Yang Fengyun Yan Hong Milao

Research on Pattern Analysis and Data Classification Methodology for Data Mining and Knowledge Discovery

Pattern Analysis and Data

Classification Methodology

Analyze the

organizational structure of network graphical pattern with the knowledge of machine learning methodolgy and graph theory

2016 R4

5 R. Adaikkalam

Dr. A. Shaik Abdul Khadir

A Survey on Data mining Techniques for Analysis of Social Network

Data Mining Techniques

Display about graphical images and mathematical or computaional models features

2016 R5

6 Kanika Mathur

Online social Network Mining

Naive Bayes Text classifier

When the texual information is not structured according to the grammatical conversation, it become more challenging

2016 R6

7 Remya R S

Smitha E S

Text

Categorization using Data Mining Technique on Social Media Data

Naive Bayes Multi- Label Classification

It is beneficial in learning analytics, educational data mining, and learning technologies

2015 R7

8

Ritu Mewari Ajit Singh Akash Srivastava

Opinion Mining Techniques on Social Media Data

Opinion

Mining Extract pearl knowledge 2015 R8

9 Faris Kateb

Jugal Kalita

Classifying Short Text in Social Media: Twitter as Case Study

Text

Classification Techniques

Challenge in classifying

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10 Pooja Sikka

Data Mining of Social

Networks using Clustering based SVM

K-Mean Clustering based SVM

SVM have not been favored for large data sets for mining

K-Mean micro-clustering technique will be implied with SVM

2015 R10

11 Snehal Ramteke

Association Rule Mining Algorithm Using Big Data Analysis

Apriori Algorithm

Improving apriori algorithm using Top-k-rule algorithm

2016 R11

12 Dr. R Nedunchezhian

K Geethanandhini

Association Rile Mining on Big Data-A Survey

Discussed various ARM algorithms for frequent itemset

Also discussed merits and demerits of those

techniques

Space and time complexities are the major issues with all algorithms discussed in this paper

2016 R12

13

Jjihyun Song Kyeongloo Kim Minsoo Lee

A Big Data Analysis and Mining approach for IoT Big Data

Authors researched about R association rule with market basket dataset

2018 R13

III. CONCLUSION

This paper provides a additional current analysis and update of social network analysis on the market. Literatures are reviewed supported completely different aspects of social network analysis. This paper studies the application of the techniques and thought of web mining for social network analysis, and reviews the connected literature concerning web mining and social networks. Social networks investigation allotted through the technique of web mining is a motivating field of research.

Acknowledgement

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REFERENCES

[1] Alexnder Pak, Patrick Paroubek,"Twitter as a corpus for sentiment analysis and opinion mining", Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, 17-23 May 2010, Valletta, Malta

[2] E.Raju K.Sravanthi, "Analysis of Social Networks using the techniques of Web Mining", International Journal of Advanced Research in Computer Science and Software Engineering (ISSN: 2277-6451), Volume 2, Issue 10

[3] Hemant Kumar Soni, Sanjiv Sharma, Pankaj K. Mishra, “Association Rule Mining: A Data Profiling and Prospective Approach”, International Conference on Futuristic Trends in Engineering, Science, Humanities, and Technology (FTESHT-16) ISBN: 978-93-85225-55-0, January 23-24, 2016, Gwalior

[4] Heling Jiang, An Yang, Fengyun Yan, Hong Milao, “Research on Pattern Analysis and Data Classification Methodology for Data Mining and Knowledge Discovery”, International Journal of Hybrid Information Technology Vol. 9, no. 3 (2016)

[5] R. Adaikkalam, Dr. A. Shaik Abdul Khadir, “A Survey on Data mining Techniques for Analysis of Social Network”, Volume 4, Issue 3, March 2016, International Journal of Advance Research in Computer Science and Management Studies.

[6] Kanika Mathur, “Online Social Network Mining”, International Journal of Computer Trends and Technology (IJCTT) – Volume 35 Number 4- May 2016

[7] Remya R S, Smitha E S, “Text Categorization using Data Mining Technique on Social Media Data”, International Journal of Advanced Research in Education & Technology (IJARET), Vol. 2, issue 4 (Oct. - Dec. 2015

[8] Ritu Mewari, Ajit Singh, Akash Srivastava, “Opinion Mining Techniques on Social Media Data”, International Journal of Computer applications (0975 – 8887) Volume 118 – No. 6, May 2015

[9] Faris Kateb, Jugal Kalita, “Classifying Short Text in Social Media: Twitter as Case Study”, International Journal of Computer applications (0975 – 8887) Volume 111 – No. 9, February 2015 [10] Pooja Sikka, “Data Mining of Social Networks using Clustering

based SVM”, International Journal of Research review in Engineering Science & Technology (ISSN: 2278-6643) Volume-4 , Issue-1, April 2015

[11] Snehal Ramteke, “Association Rule Mining Algorithm Using Big Data Analysis”, International Journal on Recent and Innovation Trends in Computing and Communication (ISSN: 2321-8169) Volume 4, Issue 5, May 2016

[12] Dr. R Nedunchezhian, K Geethanandhini, “Association Rile Mining on Big Data-A Survey”, International Journal of Engineering Research and Technology (ISSN: 2278-0181), Volume 5, Issue 5, May 2016