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Home»Articles»Optimization of Microblog Representation Using Deep Learning Approach

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Optimization of Microblog Representation Using Deep Learning Approach

Author : K. H. Walse
Volume 7 No.2 July-September 2018 pp 133 - 138

Abstract

Microblogging, as a new form of online communication in which users talk about their daily lives, to gather real-time news, opinion about people or share information by short posts, has become one of the most popular social networking services today, e.g. to stay in touch with friends . Finding proper representations of microblog texts is a challenging issue. The overview of microblog included how to extract information from microblog, also discuss about Microblogging and Twitter. This paper included existing research on Optimization of Microblog Representation by using existing techniques and methods as well as microblogging services.Current approaches, for microblog reduction are single indexing. In this work, we have proposed double indexing method for microblog reduction. We have used our own generated dataset for microblog reduction. We have collected tweets from real time twitter for both single and double indexing technique. Also, we have use LDA with Semantic Similarity algorithm. We compared our results with single indexing. Experimental results shows that double indexing gives better performance than single indexing. This may happen because we will show both original and reduced tweets, so it will be known to user which tweet are removed and relevant part shows as output. We have also compared our results with the state-of-art. The proposed double indexing gives better performance than existing 1-ROCA of improved online SVM technique.

Keywords

Microblogs, Twitter, Text representation, Sentiment analysis, Topic detection, Clustering, SVM

Full Text:

References

[1] Lei Xu, Chunxiao Jiang, Senior Member, IEEE, Yong Ren, Member, IEEE, and Hsiao-Hwa Chen, Fellow, IEEE, “Microblog Dimensionality Reduction—A Deep Learning Approach”,
InKnowledge and Data Engineering, 2016, IEEE Transaction, Vol. 28, No.7,pp. 1779-1789, 2016.
[2] Zhi-Qiang Xian, Y. X. Zou and Xin Wang,“Sentiment Analysis of Chinese Micro-blog Using Vector Space Model”, 2014.
[3] Fotis Aisopos, George Papadakis, Konstantinos Tserpes and Theodora Varvarigou, “Content vs. Context for Sentiment Analysis:a Comparative Analysis over Microblogs”, In Proceedings of the 6th ACM, p. 12, pp. 187-196, ACM 2012.
[4] Xiaoqian Lui and Tingshao Zhu, “Deep learning for constructing microblog behavior representation to identify social media user’s personality”, In PeerJ Comput. Sci. 81, 2016, pp. 1-15, 2016.
[5] Bongwon Suh, Lichan Hong, Peter Pirolli, and Ed H. Chi , “Want to be Retweeted? Large Scale Analytics onFactors Impacting Retweet in Twitter Network.” IEEE International Conference on Social Computing, 2010, pp. 177 – 184, 2013.
[6] Xia Yan and Hua Zhao, “ Chinese Microblog Topic Detection Based on the Latent Semantic Analysis and Structural Property.” In JOURNAL OF NETWORKS, Vol. 8, No. 4, pp. 917-923, 2013.
[7] Xia Hu, Lei Tang and Huan Liu, “Enhancing Accessibility of Microblogging Messages Using Semantic Knowledge.” pp. 2465-2468, 2011.
[8] Mr. M. Vengateshwaran, Mrs. C. Ramyapriyadarsini and Ms. N. Valarmathi, “Efficient Deep Learning Approach for Dimensionality Reduction using Micro blogs from Big data”, IJRASET, Vol. 5, Issue 2, pp. 5-10,2017.
[9] Ms. Payal R. Rathi and Dr. K. H. Walse, “Survey on optimization of microblog Representation”, In Proceedings International Conference- EECCMC 2018, pp. 1-8, Jan 28 and Jan 29, 2018.
[10] Jiliang TANG, Xufei WANG, Huiji GAO, Xia HU, Huan LIU, Computer Science & Engineering, Arizona State University, Tempe, AZ 85281, USA“Enriching short text representation in microblog for clustering”, In Front. Comput. Sci., Vol. 6, No. 1, pp. 1-13, 2012.
[11] Kishor H. Walse, Rajiv V. Dharaskar and Vilas M. Thakare “A Study on the Effect of Adaptive Boosting on Performance of Classifiers for Human Activity Recognition”, InProceedings of the International Conferenceon Data Engineering and Communication Technology, Advances in Intelligent Systems and Computing, Vol. 469, pp. 419-429, 2017.
[12] Shrunkhala Satish Wankhede, Ms. S. A. Chhabria and Dr. R. V. Dharaskar “Contolling Mouse Cursor Using Eye Movement”, In IJAIEM, pp. 1-7, 2013.
[13] K. H. Waslse and D. R. Dhotre “Wireless Network: Performance Analysis of TCP”, Information Technology Journal, Vol. 6, No. 3, pp. 363-369, 2007.
[14] Hui Ning, Song Li, Fanhu Zeng and Li Xu “Research on Microblog Filtering Technology Based on Improved Online Support Vector Machine Model0”, IEEEInternational Conference on Mechatronics and Automation, pp. 2326- 2332, 2016.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

Foundations of High-performance ComputingTheory of algorithms and computability

Parallel & distributed computing

Computer networks

Neural networks

LAN/WAN/MAN

Database theory & practice

Mobile Computing for e-Commerce

Future Internet architecture

Protocols and services

Mobile and ubiquitous networks

Green networking

Internet content search

Opportunistic networking

Network applications

Network scaling and limits

Artifial Intelligences

Pattern/Image Recognitions

Communication Network

Information Security

Knowledge Management

Management Information systems

Multimedia communicatiions

Operations research

Optical networks

Software Engineering

Virtual reality

Web Technologies

Wireless technology

Microblogging, as a new form of online communication in which users talk about their daily lives, to gather real-time news, opinion about people or share information by short posts, has become one of the most popular social networking services today, e.g. to stay in touch with friends . Finding proper representations of microblog texts is a challenging issue. The overview of microblog included how to extract information from microblog, also discuss about Microblogging and Twitter. This paper included existing research on Optimization of Microblog Representation by using existing techniques and methods as well as microblogging services.Current approaches, for microblog reduction are single indexing. In this work, we have proposed double indexing method for microblog reduction. We have used our own generated dataset for microblog reduction. We have collected tweets from real time twitter for both single and double indexing technique. Also, we have use LDA with Semantic Similarity algorithm. We compared our results with single indexing. Experimental results shows that double indexing gives better performance than single indexing. This may happen because we will show both original and reduced tweets, so it will be known to user which tweet are removed and relevant part shows as output. We have also compared our results with the state-of-art. The proposed double indexing gives better performance than existing 1-ROCA of improved online SVM technique.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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