information retrieval topics in twitter using weighted prediction network

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International Journal of Civil Engineering and Technology (IJCIET)Volume 8, Issue 1, January 2017, pp.

Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=1

ISSN Print: 0976-6308 and ISSN Online: 0976

© IAEME Publication Scopus

INFORMATION RETRIEVA

USING WEIGHTED PREDI

AL

ABSTRACT

Social networking site is a platform to share valuable information with friend, colleague or

others. These social networking sites store data various format as the nature of data is

heterogeneous. This data or information can be effectively utilized to improve the experience of

user.

To do this intensive analysis of data available at social networking site is necessary. As this

data, can be well interpreted in the form of a graph, so with the help of graph

resources could be connected to derive or conclude the information.

This is the intent behind the experiment performed to design weighted social networking

prediction network. This network helps to find the tweets regarding the respectiv

providing the weightage. The data displayed is displayed according to the descending order of

this weightage.

Key words: Social Networking, Topic Analysis, Data Mining Using Prediction Network, Tags,

Trends, Information Extraction

Cite this Article: Boshra F. Zopon AL_Bayaty, Information Retrieval Topics In Twitter Using

Weighted Prediction Network

8(1), 2017, pp. 781–788.

http://www.iaeme.com/IJCIET/issues.

1. INTRODUCTION

Social networking sites contain the valuable data because user has created the data or user is directly

associated the respective data, hence the analysis of these with social networking site could reveal

extremely important source or a form of data. In case of experiment that is to be performed is based on

the analysis of social networking site; by considering trend as a base and deriving all associated

tweets. Some time the user, who performs the tweet is important, so users also m

and trends. This information helps to connect or decide the interest. Decision making process could be

well supported by the effective means of social networking site. Social networking sites are reach

source of information related with user. This information can be analyzed and utilized for welfare of

the users.

The tag analysis does the analyses of various tags that are topics mentioned on twitter, these tags

are mapped with users. The related logical graphs are plotted (prepared) s

chain model. The novelty of this work maps users, tags, topics, trender, and follower. The mappings

IJCIET/index.asp 781

International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 1, January 2017, pp. 781–788 Article ID: IJCIET_08_01_092

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=1

6308 and ISSN Online: 0976-6316

Scopus Indexed

INFORMATION RETRIEVAL TOPICS IN TWITTER

USING WEIGHTED PREDICTION NETWORK

Boshra F. Zopon AL_Bayaty

-Mustansiriyah University, Baghdad, Iraq



a or information can be effectively utilized to improve the experience of


data, can be well interpreted in the form of a graph, so with the help of graph

resources could be connected to derive or conclude the information.


prediction network. This network helps to find the tweets regarding the respectiv


Social Networking, Topic Analysis, Data Mining Using Prediction Network, Tags,

Trends, Information Extraction.

Boshra F. Zopon AL_Bayaty, Information Retrieval Topics In Twitter Using

Weighted Prediction Network. International Journal of Civil Engineering and Technology




or a form of data. In case of experiment that is to be performed is based on


tweets. Some time the user, who performs the tweet is important, so users also m



th user. This information can be analyzed and utilized for welfare of


are mapped with users. The related logical graphs are plotted (prepared) suggested by using Markov


[email protected]


L TOPICS IN TWITTER

CTION NETWORK



a or information can be effectively utilized to improve the experience of


data, can be well interpreted in the form of a graph, so with the help of graph various valuable


prediction network. This network helps to find the tweets regarding the respective trend by


Social Networking, Topic Analysis, Data Mining Using Prediction Network, Tags,

Boshra F. Zopon AL_Bayaty, Information Retrieval Topics In Twitter Using

International Journal of Civil Engineering and Technology,

IType=1



or a form of data. In case of experiment that is to be performed is based on


tweets. Some time the user, who performs the tweet is important, so users also mapped with the tweets



th user. This information can be analyzed and utilized for welfare of


uggested by using Markov


Information Retrieval Topics In Twitter Using Weighted Prediction Network

http://www.iaeme.com/IJCIET/index.asp 782 [email protected]

are also facilitated by weightage. This means user will get the priorities tags, topics and related users;

according to the weightage.

The suggested prediction network not only analyze an information on social networking sites but

also maps valuable tags and person to associate a user with invaluable information in the form of

suggestions.

2. LITREATURE SURVEY

Social networking is a platform where the entire persons like to spend the time to share information;

achievements in life, opinion on some topic can be expressed. In short it is the comfortable form of

communication.

There are number of social networking platform for the communication, two survey carried out at

Australia shows that 52% of usage of an internet has been increased due to social networking sites.

Total amount of Overall access of an internet due to social networking site is 79%. Also 49% of

internet is daily used in comparison of overall internet usage per day.

There are 45% people who use social networking site on an average 297 persons are connected

with every person. 70% mobile holders use an internet in the form of social networking sites.

Three social networking sites are successful to engage people on an internet, namely: Twitter,

Facebook, and LinkedIn. Statistics collected in year 2013 says that on an average 118 users follow a

count and 52% of total number of users tweets at least once in a week.

The relation, association are mentioned by using Graph, so one basic concepts needed to do the

social networking analysis using graphs are as below:

1. Vertices:� It could be represented with user.

2. Edge:� Edge is the connection among nodes. Here Edge could be used to represent weight.

There is some important measurement applied to know the association, relation and following

metrics are used.

1. Density of network:�� If there are more number of ties in a graph, then graph is known as Dense

graph. The graph which contains less number of ties are known as spars graphs.

2. Betweenanes:�� The route with the help of which two nodes are connected directly. In case of social

networking analysis, these nodes could be person or trend.

3. Closeness:�� This is the path through which one node is connected with another node.

There are different types of networking with various structures. According to the nature of the

graph the analysis of social networking analysis need to be made.

The example of a graph is mentioned below:

Figure 1 Simulated graph of association of data of social networking site – Twitter



This is a graph G (V, E), where V indicates vertices A,B,C,D and E indicates edges AB, BC, CD,

DE, EF, FA, BF, BE, CF, CE. Here the node may represent object and edges mention relationship

amongst it. Simulation of social networking websites could be made with the help of a graph which

may help to analysis the relationship among the node.

There are number of approaches to suggest or search the relevant record based on the information

like graph. Members are suggested with the adequate relationship by considering the extent of

relationship as well.

There are different types of graphs present with and without weight. There are social networking

sites like twitter, the where the dummy records could be easily created and with the help of it analysis

could be done problem in case of existing structure is there is no prediction made to calculate interest.

Figure 2 A-graph without weight B: Graph with weight

3. PROPOSED ARCHITECTURE

Figure 3 Weighted prediction network for information retrieval from Twitter

Proposed architecture says that the social networking site helps to collect data from social

networking site. The collected data is stored in a database.

The stored data helps to analysis the data by the means of weighted prediction network, the trends

are analyzed and the best suitable data is revealed to the user. The important or significant change



could be observed in the form reduction in time span required to search similar or relevant record from

a same data source like twitter. This type of valuable information generated with the help of the data

generated by the model mentioned above. This optimized structure of the prediction network helps to

suggest the topics relate with the trends displayed on account.

Figure 4 Weighted framework for trends and tweets

The graph mentioned above gives detailed functioning of weighted prediction network. This graph

is nothing but the instance or snapshot of actual network.

Consider example mentioned above indicates the trends mentioned in a twitter. The logic used to

display the trend is a recent topic, but user may be interested in number of topics which may not be

mentioned in the list of trends. Also what sort of logic or technique to be utilized to display the trends

and their sequence.

In the graph mentioned above A….G, represents the various trends available on twitter.

Considering that A is a trend which is available; Most relevant trend is to be collected on every edge

the weights are available unlike of approach used in Markov chain model, where the node which is

generally achieved by using shortest path is calculated. This type of approach is useful when reach

ability or communication in a time is main concern. The aim of this experiment is to identify a trend

associated with current thread. Here ABCJGF is the path considering the relationship among node

with the higher weight out of all available node is considered as next trend. in this way the associated

node is identified as a the strongest associated node with a node A. In the graph mentioned above the

A is associated with AE, AC, and AB. In this association, AB>AE>AC, there AB association is

considered, so B node will get displayed as most relevant trend.

The input for this experiment is the credentials of twitter account; that is user need to have twitter

account. Once user Logged in into the system the list of recent trends is displayed with the help of

procedure mentioned above thus user will be explored only to the recent trends. With the help of

experiment performed the suggestion or the prediction of the most suitable trend is made to avail the

facilities or data source provided by the twitter.

Thus, the novel approach helps to display most relevant trends to the respective user as per the data

fetched by the system. In this application, the efforts required to search the relevant data is minimized

and this could be also verified or tallied by the amount of time span spent by the respective user on the

respective trend which is displayed trend which is displayed by weightage based prediction network.



4. THE WEIGHTED PREDICTION ALGORITHM

Box: The weighted prediction algorithm

Data Structure

U---� User (with user’s credentials)

T---� Recent trends

Tw---� Twitter data source

TT---� Total number of trends

Twj, Twi ---� Weightage of association among trend.

TLi ---� List with descending order of weightage of trends.

5. EXPERIMENTAL SETUP

To perform this work, spring framework is used, this framework designed to facilitate the effective

and efficient programming by separating business applications. The data is stored in MySQL with the

help of the hibernate which maps object with database Eclipse Kepler is used, this is the version of

eclipse a IDE (Integrated development Environment) used develop applications in J2EE spring

framework. To execute the web applications designed using Java, Tomeat-7.0 server is used as web

server.

6. IMPLEMENTATION AND TESTING

To perform the experiment spring, MVC architecture and framework is used. Advantage of this

technology is, one can modify view data or logic without affecting or bothering the associated

technologies. For that the spring framework is used. This is the web development for enterprise

(internet) using Java technology. Model view controller (MVC) approach is followed to perform this

experiment. Also, twitter APIs are used ensure communication with account and respective app on

twitter, this application program provide an interface to connect programs with different technologies.

Twitter account is needed along with API to fetch the information available on a twitter. Job of

prediction network is to understand the requirement of user and make the availability of information in

the form of tweets, trends and associated person (that is source of the information). The

implementation and testing face shown in screenshots below:

Step 1: Fetch <U, <T>>

Where U, <T> Tw

Step 2: Consider logical Graph of size TT

Where TT Tw

Step 3: For T1…TT

Twi>Twj

Where i 1… TT

j 1…TT

Li = Ti

Step 4: For L1 … LT

Display Li



Figure 5 Some screenshot related to implementation and testing face

7. RESULT

Recent topics are called as a trend, these trends are fetched with the person who initiate and tweet on

the trends, snapshot of the data is mentioned below:



Screenshot of Table -1 User – Recent trends

Social communication delivering useful data is segregated in adroit manner; structure of collecting

this data can be seen in a table mentioned below.

Screenshot of Table –2 Trends – Tweet s – user



8. CONCLUSION

Based on the work experiment performed the weighted prediction network helps to fetch the highly-

associated tweets from data source-twitter. This helps to effectively prioritize different tweets

available. Also, user can respond by inserting new tweets, which will get reflected to his/her original

twitter account. This work can be useful to minimize the information in the form of tweets.

Human nature is to share thoughts, pictures, Achievements with friends, colleague, and relatives.

Social networking site is a platform to achieve the publicity thus the huge amount of data becomes

source of information.

ACHNOWLEDGMENT

I would like to thank Al_mustansiriyah university, (www.uomstansiriyah.edu.iq), Baghdad, Iraq, for

its support in the present work and to inspire me always.

REFERENCES

[1] Sensis, Social Media Report May 2015, Rob Wong, sensis,, The Digital Industry Association of

Australia,

[2] Digital Marketing Analytics with a Competitive Edge, Social Media Trends 2015.

[3] Visualizing Big Data: Social Network Analysis, Michael Lieberman, Digital research conference,

2014.

[4] The use of Social Network Analysis in Innovation Research: A literature review Fabrice Coulon,

PhD Candidate1 Division of Innovation - LTH Lund University, Sweden, January 17th 2005.

[5] Shamanth Kumar Fred Morstatter Huan Liu Twitter Data Analytics, Springer, August 19, 2013.

[6] Sentiment Analysis of Twitter Data Apoorv Agarwal Boyi Xie Ilia Vovsha Owen Rambow

Rebecca Passonneau 2011.

[7] Massive Social Network Analysis: Mining Twitter for Social Good David Ediger Karl Jiang Jason

Riedy David A. Bader Georgia Institute of Technology Atlanta, GA, USA Courtney Corley Rob

Farber Pacific Northwest National Lab. Richland, WA, USA William N. Reynolds Least Squares

Software, Inc. Albuquerque, NM, USA, 2010 39th International Conference on Parallel Processing,

2010.

[8] Sentiment Knowledge Discovery in Twitter Streaming Data Albert Bifet and Eibe Frank University

of Waikato, Hamilton, New Zealand.

[9] Visual Sentiment Analysis on Twitter Data Streams Ming Hao, Christian Rohrdantz, Halldór

Janetzko, Umeshwar Dayal Daniel A. Keim, Lars-Erik Haug, Mei-Chun Hsu Hewlett-Packard Labs

and University of Konstanz.

[10] Boshra F. Zopon AL_Bayaty and Shashank Joshi, Empirical Implementation Naive Bayes

Classifier For Wsd, Using Wordnet. International Journal of Civil Engineering and Technology,

5(8), 2014, pp. 25–31.

[11] M.Rajeswari, Influence of Social Networking Sites on Personal and Professional Lives of People.

International Journal of Advanced Research in Engineering and Technology, 5(3), 2014, pp. 64–72.

[12] Nana Kwame Gyamfi, Makafui Nyamadi, Prince Appiah, Dr. Ferdinand Katsriku and Dr. Jama-

Deen Abdulai, A Brief Survey of Mobile Forensics Analysis on Social Networking Application.

International Journal of Computer Engineering and Technology, 7(4), 2016, pp. 81–86.

[13] How we analyzed Twitter social media networks with NodeXL, PEW research center, 2014.