Detecting the magnitude of depression in Twitter users using sentiment analysi.
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| Title: | Detecting the magnitude of depression in Twitter users using sentiment analysi. |
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| Authors: | Stephen, Jini Jojo1, jini.stephen@cs.christuniversity.in, P., Prabu1 |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708); Aug2019 (Part II), Vol. 9 Issue 4, p3247-3255, 9p |
| Database: | Applied Science & Technology Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 137154429 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=137154429 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v9i4.pp3247-3255 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 3247 Titles: – TitleFull: Detecting the magnitude of depression in Twitter users using sentiment analysi. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Stephen, Jini Jojo – PersonEntity: Name: NameFull: P., Prabu IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2019 (Part II) Type: published Y: 2019 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 9 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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