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Home»Articles»An Innovative Method of Estimation Hewing for Invention Report Mining and Estimation Summarization

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Asian Journal of Computer Science and Technology (AJCST)

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

An Innovative Method of Estimation Hewing for Invention Report Mining and Estimation Summarization

Author : S. Ravichandran and J. Sathiamoorthy
Volume 9 No.2 July-December 2020 pp 45-50

Abstract

With the assistance of Web 2.0, the bases on client interest, posting on the web surveys has become an undeniably mainstream path for individuals to impart their perspectives to different client’s suppositions and conclusions toward items and administrations. It turns into a typical practice for online business sites to give the offices to individuals to convey and distribute their audits between them. These online audits present an abundance of data on the Services and Products, which will encourage the improvement of their business. Consequently a developing number of late examinations have been centred on the Opinion Mining. For example the Opinion Mining alludes to computational method for assessing the sentiments that are mined from different Web Sources.

A couple of Opinion Mining based techniques have been considered and broke down. From our investigation, it is seen that a couple of feeling mining based directed and unaided techniques had not delivered great outcomes because of alluding less number of sentiments inside a similar URL’S and treating the highlights with comparable significance as various. To beat this issue, Topic Anatomy Model TSCAN was proposed, where the Task is called as Topic Anatomy and which sums up and relates the primary pieces of a point with the goal that the per users could comprehend the substance without any problem.

By utilizing this model, the more data can be removed and related through their transient closeness, which will give conceivable substance. This model is including imperative part in the Opinion Mining since clients can impart their insights about the items. From our usage, it is seen that this plan gives the best reasonable answer for the client’s advantages and requests. Notwithstanding, it burns-through more opportunity to anticipate the best performing items because of huge informational collections respectively.

Consequently our exploration work is proposed and actualized a productive strategy for Opinion Mining called an Efficient Parallel Opinion Mining (EPOM) constructed TSCAN Algorithm separately. It is centring more sites and it is removing more data in equal way, so we can get advanced productive outcome with least execution time. From our outcomes, it is noticed that it gives the best reasonable answer for the client’s advantages and requests and it I s improving the presentation of existing method regarding Quality of Information, Prediction and Execution Time.

Keywords

Text Mining, Sentiment classification, summarization, URL, reviews

Full Text:

References

[1]   Minqing Hu and Bing Liu.  “Mining and summarizing customer reviews”, In: Proc. of the 10th ACM SIGKDD-2016 international conference on knowledge discovery and data mining, Seattle, pp 168–177.

[2]   N. Kobayashi,  R. Iida, K. Inui andY. Matsumotto. “Opinion extraction using a learning-based anaphora resolution technique”, In Proc. of the second international joint conference on natural language processing (IJCNLP-04), Jeju Island, pp 173–178

[3]   Wong TL, W. Lam “Learning to extract and summarize hot item features from multiple auction Web sites”, Knowl Inf Syst, Vol.14, No.2, pp.143–160, 2008.

[4]           E. Riloff.,   W. Janyce,   and W. Theresa,  “Learning Subjective Nouns Using Extraction Pattern Bootstrapping” In Proc.7th   Conf.   Natural   Language   Learning, pp 25-32, 2009.

[5] Yuanbin Wu, Qi Zhang, Xuanjing Huang and LideWu,  “Phrase Dependency Parsing for Opinion Mining”, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Singapore, 6-7 August 2018, pp.1533–1541.

[6] Parma Nand, “On the use of Salience Weights in Anaphora Resolution”, In Proc. of NZCSRSC 2019, April 2008, Christchurch, New Zealand.

[7]   Chih-Ping Wei, Yen-Ming Chen, Chin- Sheng Yang, Christopher and C. Yang. “Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews”, Springer-Verlag, 2019.

[8]   G. Carenini and A. Pauls, “Multi- document Summarization of Evaluative Text”, In Proc. 11th Conf. European Chapter of the ACL, 2019.

[9]   M. Gamon., A. Aue., S. Corston-Oliver and E. Ringger, “Pulse: Mining Customer Opinions from Free Text”, In Proc. 6th Int. Symp. Advances in intelligent data analysis, pp121–132, 2005.

[10]         J.Wiebe, “Learning Subjective Adjectives from Corpora”, In Proc. of 12th Conference on   Innovative   Applications   of   Artificial Intelligence, 2019.

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

With the assistance of Web 2.0, the bases on client interest, posting on the web surveys has become an undeniably mainstream path for individuals to impart their perspectives to different client’s suppositions and conclusions toward items and administrations. It turns into a typical practice for online business sites to give the offices to individuals to convey and distribute their audits between them. These online audits present an abundance of data on the Services and Products, which will encourage the improvement of their business. Consequently a developing number of late examinations have been centred on the Opinion Mining. For example the Opinion Mining alludes to computational method for assessing the sentiments that are mined from different Web Sources.

A couple of Opinion Mining based techniques have been considered and broke down. From our investigation, it is seen that a couple of feeling mining based directed and unaided techniques had not delivered great outcomes because of alluding less number of sentiments inside a similar URL'S and treating the highlights with comparable significance as various. To beat this issue, Topic Anatomy Model TSCAN was proposed, where the Task is called as Topic Anatomy and which sums up and relates the primary pieces of a point with the goal that the per users could comprehend the substance without any problem.

By utilizing this model, the more data can be removed and related through their transient closeness, which will give conceivable substance. This model is including imperative part in the Opinion Mining since clients can impart their insights about the items. From our usage, it is seen that this plan gives the best reasonable answer for the client's advantages and requests. Notwithstanding, it burns-through more opportunity to anticipate the best performing items because of huge informational collections respectively.

Consequently our exploration work is proposed and actualized a productive strategy for Opinion Mining called an Efficient Parallel Opinion Mining (EPOM) constructed TSCAN Algorithm separately. It is centring more sites and it is removing more data in equal way, so we can get advanced productive outcome with least execution time. From our outcomes, it is noticed that it gives the best reasonable answer for the client's advantages and requests and it I s improving the presentation of existing method regarding Quality of Information, Prediction and Execution Time.

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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