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An Approach for Big Data to Evolve the Auspicious Information from Cross-Domains

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 2 (2017)

DOI
10.11591/ijece.v7i2.pp967-974
ISSN
2088-8708, 2088-8708
Published
2017-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Sentiment analysis is the pre-eminent technology to extract the relevant information from the data domain. In this paper cross domain sentimental classification approach Cross_BOMEST is proposed. Proposed approach will extract † ve words using existing BOMEST technique, with the help of Ms Word Introp, Cross_BOMEST determines † ve words and replaces all its synonyms to escalate the polarity and blends two different domains and detects all the self-sufficient words. Proposed Algorithm is executed on Amazon datasets where two different domains are trained to analyze sentiments of the reviews of the other remaining domain. Proposed approach contributes propitious results in the cross domain analysis and accuracy of 92 % is obtained. Precision and Recall of BOMEST is improved by 16% and 7% respectively by the Cross_BOMEST.

Collaboration network of the authors

9 authors · 19 co-authorship links · 7 across different organisations

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  • Guru Gobind Singh Indraprastha University
  • COER University
  • Iraqi Board for Medical Specializations
  • University of Wasit
  • Bhagwant University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Puneet Singh LambaGuru Gobind Singh Indraprastha University1
Ansam A. AbdulhussienIraqi Board for Medical Specializations1
Oday Ali HassenUniversity of Wasit1
Charu GuptaBhagwant University1
Akshara NairAffiliation unknown1
Prachi RaniAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>An Approach for Big Data to Evolve the Auspicious Information from Cross-Domains</dc:title><dc:creator>Arora, Preeti</dc:creator><dc:creator>Virmani, Deepali</dc:creator><dc:creator>Kulkarni, P.S.</dc:creator><dc:description>&lt;jats:p&gt;Sentiment analysis is the pre-eminent technology to extract the relevant information from the data domain. In this paper cross domain sentimental classification approach Cross_BOMEST is proposed. Proposed approach will extract &amp;lt;strong&amp;gt;†&amp;lt;/strong&amp;gt;ve words using existing BOMEST technique, with the help of Ms Word Introp, Cross_BOMEST determines &amp;lt;strong&amp;gt;†&amp;lt;/strong&amp;gt;ve words and replaces all its synonyms to escalate the polarity and blends two different domains and detects all the self-sufficient words. Proposed Algorithm is executed on Amazon datasets where two different domains are trained to analyze sentiments of the reviews of the other remaining domain. Proposed approach contributes propitious results in the cross domain analysis and accuracy of 92 % is obtained. Precision and Recall of BOMEST is improved by 16% and 7% respectively by the Cross_BOMEST.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v7i2.pp967-974</dc:identifier><dc:identifier>10.11591/ijece.v7i2.pp967-974</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 2 (2017); 967</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/6448/6316</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>