View in EDS

Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design.

Saved in:
Bibliographic Details
Title: Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design.
Authors: You, Linlin, Tuncer, Bige, Xing, Hexu
Source: IEEE Transactions on Knowledge & Data Engineering. 2/1/2019, Vol. 31 Issue 2, p343-356. 14p.
Subjects: Data, Statistics, Data analysis, Sentiment analysis, Social media
Abstract: The intelligence of Smart Cities (SC) is represented by its ability in collecting, managing, integrating, analyzing, and mining multi-source data for valuable insights. In order to harness multi-source data for an informed place design, this paper presents “Public Sentiments and Activities in Places” multi-source data analysis flow (PSAP) in an Informed Design Platform (IDP). In terms of key contributions, PSAP implements 1) an Interconnected Data Model (IDM) to manage multi-source data independently and integrally, 2) an efficient and effective data mining mechanism based on multi-dimension and multi-measure queries (MMQs), and 3) concurrent data processing cascades with Sentiments in Places Analysis Mechanism (SPAM) and Activities in Places Analysis Mechanism (APAM), to fuse social network data with other data on public sentiment and activity comprehensively. As proved by a holistic evaluation, both SPAM and APAM outperform compared methods. Specifically, SPAM improves its classification accuracy gradually and significantly from 72.37 to about 85 percent within nine crowd-calibration cycles, and APAM with an ensemble classifier achieves the highest precision of 92.13 percent, which is approximately 13 percent higher than the second best method. Finally, by applying MMQs on “Sentiment&Activity Linked Data”, various place design insights of our testbed are mined to improve its livability. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 134073127
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22You%2C+Linlin%22">You, Linlin</searchLink><br /><searchLink fieldCode="AR" term="%22Tuncer%2C+Bige%22">Tuncer, Bige</searchLink><br /><searchLink fieldCode="AR" term="%22Xing%2C+Hexu%22">Xing, Hexu</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Knowledge+%26+Data+Engineering%22">IEEE Transactions on Knowledge & Data Engineering</searchLink>. 2/1/2019, Vol. 31 Issue 2, p343-356. 14p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Data%22">Data</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The intelligence of Smart Cities (SC) is represented by its ability in collecting, managing, integrating, analyzing, and mining multi-source data for valuable insights. In order to harness multi-source data for an informed place design, this paper presents “Public Sentiments and Activities in Places” multi-source data analysis flow (PSAP) in an Informed Design Platform (IDP). In terms of key contributions, PSAP implements 1) an Interconnected Data Model (IDM) to manage multi-source data independently and integrally, 2) an efficient and effective data mining mechanism based on multi-dimension and multi-measure queries (MMQs), and 3) concurrent data processing cascades with Sentiments in Places Analysis Mechanism (SPAM) and Activities in Places Analysis Mechanism (APAM), to fuse social network data with other data on public sentiment and activity comprehensively. As proved by a holistic evaluation, both SPAM and APAM outperform compared methods. Specifically, SPAM improves its classification accuracy gradually and significantly from 72.37 to about 85 percent within nine crowd-calibration cycles, and APAM with an ensemble classifier achieves the highest precision of 92.13 percent, which is approximately 13 percent higher than the second best method. Finally, by applying MMQs on “Sentiment&Activity Linked Data”, various place design insights of our testbed are mined to improve its livability. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=134073127
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/TKDE.2018.2828431
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 343
    Subjects:
      – SubjectFull: Data
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Sentiment analysis
        Type: general
      – SubjectFull: Social media
        Type: general
    Titles:
      – TitleFull: Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: You, Linlin
      – PersonEntity:
          Name:
            NameFull: Tuncer, Bige
      – PersonEntity:
          Name:
            NameFull: Xing, Hexu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: 2/1/2019
              Type: published
              Y: 2019
          Identifiers:
            – Type: issn-print
              Value: 10414347
          Numbering:
            – Type: volume
              Value: 31
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: IEEE Transactions on Knowledge & Data Engineering
              Type: main
ResultId 1