Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework.
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| Title: | Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework. |
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| Authors: | Ramasamy, Mekala1, mekalars@gmail.com, Elangovan, Mohanraj2 |
| Source: | Web Intelligence (2405-6456); Aug2025, Vol. 23 Issue 3, p295-318, 24p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 186776453 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Ramasamy%2C+Mekala%22">Ramasamy, Mekala</searchLink><relatesTo>1</relatesTo>, <i>mekalars@gmail.com</i><br /><searchLink fieldCode="AU" term="%22Elangovan%2C+Mohanraj%22">Elangovan, Mohanraj</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Web+Intelligence+%282405-6456%29%22">Web Intelligence (2405-6456)</searchLink>; Aug2025, Vol. 23 Issue 3, p295-318, 24p |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=186776453 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/WEB-230096 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 295 Titles: – TitleFull: Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ramasamy, Mekala – PersonEntity: Name: NameFull: Elangovan, Mohanraj IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 24056456 Numbering: – Type: volume Value: 23 – Type: issue Value: 3 Titles: – TitleFull: Web Intelligence (2405-6456) Type: main |
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