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isr final Paper

Words: 1583, Paragraphs: 249, Pages: 6

Paper type: Essay , Subject: Latest Image Processing

Social Media Analysis Methods and Applications

Manasvi Sharma


Amity Institute of Information Technology

Amity University

Noida, Uttar Pradesh

sharmamanasvi. [email protected]


Social Media has drawn exceptional attention

in the current decade. Social networking sites

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like Twitter, Facebook are access ed via the

inter net . People greatly rely on social media

for news, data and opinions of others on

numerous subject matte rs. This causes the

generation of large facts characteriz ed via

three problems specifically: length, noise and

dynamism making the social network data

very complex to analyze manually, for which

a beneficial concept of Data Mining comes

into picture. Data mining provides a huge

range of techniques for detecting beneficial

knowledge from massive datasets like

tendencies, patterns and rules [1]. Data

mining techniques are used for records

retrieval, statistical modelling and system

mastering. These strategies appoint

information pre -processing, statistics

evaluation, and statistics interpretation

procedures inside the direction of statistics

analysis. Social media analytics also plays a

critical role in e-trade for retrieving the

beneficial records of a produc ts or services.

Social media evaluation affords each a visible

and a mathematical evaluation of human

relationships. Web can also be considered as a

social media. Social networks are fashioned

between Web pages by means of hyperlinking

to different Web pag es. There are many

career opportunities related to social media

analysis and even many big organizations are

still engaged in projects related directly or in-

directly to it. The motive of this paper is to

review the innovation research literature

which ha s made an express use of social

community evaluation methodology on the

way to provide empirical support to

Innovation al theori es or conceptual

frameworks. Th is evaluation introduces social

network analysis then discusses why and how

it has been utilized in inn ovation research thus


Keywords – Data Mining, Big data analysis,

Real time analysis


Social media is a network of different internet

site s enabling the user to talk with each other

by publish ing , films, remarks and ma ny

others. Also, these are internet -based services

that allow people to grow their public profile

and interact with other users inside that

network. Social community has improved by

using and allowing the formation and

exchange of User -Generated Content. Social

community is a graph which incorporates the

nodes and hyperlinks representing the social

relations on social networking websites [1 ], as

shown in the fig(a) below


As this paper highlights earlier that the size of

the data we need to analyze in social media is

very large and complex so there is a concept

called “Big Data” in the field of “Data

Mining” by using which it becomes easy for

us to analyze . The term Big Data is used for

large volume of facts sets whose length is so

big that an everyday software tool cannot

gather, arrange and technique it within a

positive time limit.[3] 3Vs (volume, range

and velocity) are three basic blocks of huge

statistics. Volume refers to the quantity of

data, variety refers back to the quantity of

forms of statistics and pace refers back to the

frequency of statistics processing. 3Vs of

volume can be better illustrated with the

figure depicted below in fig(b)


Social networks are vital sources of on -line

interactions and contents sharing [4 ], [5],

subjectivity [6], tests [fifty two], methods

[10], evaluation [forty eight], influences [9],

observations , feelings , opinions and

sentiments expressions borne out in textual

content, opinions, blogs, discussions,

information, feedback, reactions, or some

other files [fifty seven]. Before the arrival of

social network, the homepages changed into

popularly used in the past due 1990s which

made it possible for co mmon internet users to

percentage data. However, the sports on

social community in recent times seem to

have converted the World Wide Web (www)

into its supposed authentic introduction.

Social community platforms allow fast

statistics exchange among users irrespective

of the area. Many firms, people or even

government of nations now observe the sports

on social network.

Literature Survey

(A)How Big Data helped increase Walmart

sales turnover [7]

Walmart changed into the arena ’s biggest

sto re in 2014 in phrases of sales. Big data

have become popular inside the enterprise.

Walmart uses records mining to discover

styles in factor of income statistics.

Information mining facilitates had been sold

together or which merchandise had been

offered earlier than the purchase of a

particular product.

(B )Real Time analysis for measuring user’s

affect on on Twitter [8]

In the final couple of years, the social medium

Twitter has become more and more famous.

Since Twitter is the most used microblogging

website with approximately 500 million

customers and 340 million tweets an

afternoon, it is a thrilling source of records.

Because Twitter is widely adopted thru all

strata, it can be seen as a great mirrored image

of what is occurring round the sector. Among

all that occurs, the latest tendencies are most

interesting for businesses. The present day

trends may be analyzed and whilst identified,

reacted to. Fr om a marketing point of view,

those state -of-the -art developments can be

used to respond with appropriate sports, like

product commercials. Analyzing tweets can

consequently be a goldmine for businesses to

create a bonus to competition. Because

Twitter is extensively adopted through all

strata, it can be seen as a terrific mirrored

image of what is occurring around the arena.

Among all that takes place, the today’s

tendencies are maximum exciting for

businesses. The modern -day tendencies may

be analyzed and when identified, reacted to.

From an advertising and marketing point of

view, these brand new traits may be used to

respond with appropriate activities, like

product classified ads. Analyzing tweets can

consequently be a goldmine for businesses to

create a bonus to competition [11 ].

Social Network Analysis and Data Mining

Data mining equipment can answer enterprise

questions that historically have been very

time consuming to resolve. Data mining of

social networks may be achieved the use of

the graph mini ng techniques including

class/topologies, prediction, performance,

pattern detection, measurement and metrics,

modelling, evolution and structure,

information processing, and communities. To

extract the facts represented in graphs we

want to outline metric s that describe the

worldwide structure of graphs, locate the

community structure of the network, and

define metrics that describe the patterns of

local interplay in the graphs, develop efficient

algorithms for mining statistics on networks,

and recognize the model of era of graphs.

Social network and its analysis is an

important subject and its miles widely spread

among many younger researchers. Social

networks research emerged from psychology,

sociology, data and graph principle. Based on

graph theoretic al concepts a social networks

translates the social relationships of

individuals as points and their relationships

because the strains connecting them.

Proposed Methodology

Research requirements should be categorized

into 3 class es for their better implementation :

1. Data

2. Analytics

3. Facilities


Researchers need online read rights to

historical and actual -time social media facts,

in particular the primary sources, to conduct

world -leading studies:

1) Social Network medi a- involves accessing

to comprehensive historic data sets and real

time access with approx 15 min of time delay

as with Thomson Reuters and Bloomberg

financial data.

2) News data – indicates accessing to historic

data and real -time news data sets possibly

wi th the concepts of educational data license.

3) Public data – indicates accessing to scrape

and archived important public data, available

through RSS feeds, blogs or open government


4) Programmable interfaces – highlights that

researchers will acce ss to simple application

programming interfaces(APIs) to trash and

store other available data sources that may not

be collected automatically


Currently, social media statistics are normally

either to be had by easy preferred exercises or

require the researcher to application their

analytics in a language including MATLAB,

Java or Python. As discussed above,

researchers require:

1) Analytics dashboards – Non -programming

interfaces are required for giving what is

probably termed as ‘deep ’ get entry to ‘raw ’


2) Holistic data analysis – Tools are required

for combining as well as conducting analytics

across multiple social media and other data


3) Data visuali zation – Researchers also

require visualization tools in which

information that has been abstracted once can

be visualis ed again in some schematic form

with the aim of communicating information

precisely as well as effectively through

graphical medium .


Lastly, the sheer volume of social media

records being generated argues for

countrywide and worldwide centers to be

climbed up to assist social media studies

1) Data storage — the volume of social media

data, current and projected, is beyond most

individual universities and hence needs to be

addressed at a national science foundat ion

level. Storage is required both for principal

data sources (e.g., Twitter), but also for

sources collected by individual projects and

archived for future use by other researchers

2) Computational facility — remotely

accessible computational facilities are also

required for: a) protecting access to the stored

data; b) hosting the analytics and visualization

tools and c) providing computational

resources such as grids and GPUs required for

processing the data at the facility rather than

transmitting it acro ss a network.


This paper has focused on the concepts of

social media engineering using data mining

and the proposed methodology (classified in

various classes) for data analysis. BigData

from various social media portals can be

extracted us ing different transportation tools

and research market analytics can be

performed on them using python, Matlab and

other languages which are further used for

experimental and enterprise research level.


1. Kagdi, H., Collard, M. L., Maletic, J. I.: A

survey and taxonomy of approaches for

mining software repositories in the

context of software evolution. J. Softw.

Maint. Evol.: Res. Pract, 19, 77 -131,


2. Borgatti, S P.: “”2 -Mode concepts in social

network analysis.”” Encyclopedia of

Complexity and System Science, 8279 –

8291, 2009.

3. Aditya Patel , Hardik Gheewala , Lalit

Nagla, ”Using Social Big Media for Customer

Analytics ”,978 -1-4799 -3064 -7/14/$31.

00 ©20 14 IEEE

4. Thompson, J B.: Media and modernity: A

social the ory of the

media. John Wiley & Sons, 2013.

5. Chelmis, C., Prasanna. VK.: Social

networking analysis: A state of the art and the

effect of semantics. Privacy, security, risk and

trust (passat), 2011 ieee third international

conference on and 2011 ieee thi rd

international conference on social computing

(socialcom). IEEE, 2011.

6. Asur, S., and Huberman, B.: “”Predicting

the future with social network.”” Web

Intelligence and Intelligent Agent Technology


International Conference on . Vol. 1. IEEE,


7. Korda, H., and Itani, Z.: Harnessing social

network for health promotion and behaviour

change. Health promotion practice, 14(1), 15 –

23, 2013.

8. Kaur, G.: Social network evaluation criteria

and influence on consumption behaviour of

the youth segment. 2013.

9. Bakshy, E., Hofman, J. M., Mason, W. A.,

Watts, D. J.: Identifying influencers on

twitter. In Fourth ACM International

10.How Big Data Analysis helped increase

Walmart’s -Sales – turnover?,

ow -big -data –

analysis -helped -incr ease -walmarts -sales –


11. [Thesis] R. de Groot, ”Data Mining for

Tweet Sentiment Classification ”, Utrecht

University, Faculty of Science, Department of

Information and Computing Sciences,


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