View in EDS

Social information discovery enhanced by sentiment analysis techniques.

Saved in:
Bibliographic Details
Title: Social information discovery enhanced by sentiment analysis techniques.
Authors: Diamantini, Claudia1 (AUTHOR) c.diamantini@univpm.it, Mircoli, Alex1 (AUTHOR) a.mircoli@pm.univpm.it, Potena, Domenico1 (AUTHOR) d.potena@univpm.it, Storti, Emanuele1 (AUTHOR) e.storti@univpm.it
Source: Future Generation Computer Systems. Jun2019, Vol. 95, p816-828. 13p.
Subjects: Sentiment analysis, Content analysis, Social networks, User-generated content, Open innovation, Information storage & retrieval systems
Abstract: Abstract In recent years, the massive diffusion of social networks has made available a large amount of user-generated content, for the most part in the form of textual data that contain people's thoughts and emotions about a great variety of topics. In order to exploit these publicly available information, in this work we introduce a social information discovery system which elaborates simultaneously over more-than-one social network in an integrated scenario. The system is designed to ensure flexibility and scalability, thus enabling for (near-)real-time analysis even in case of high rates of content's creation and large amounts of heterogeneous data. Furthermore, a noise detection technique ensures a high relevance of analyzed posts/tweets to the domain of interest. We also propose a lexicon-based sentiment analysis algorithm to extract and measure users' opinion, in order to support collaboration and open innovation. Polysemous words and negations are typically challenging for lexicon-based approaches: for this reason, we introduce both a word sense disambiguation algorithm and a negation handling technique. Experiments on several datasets have proven that the combined use of both techniques improves the classification accuracy on 3-class sentiment analysis. Highlights • Social networks provide a large amount of analyzable user-generated content (USG). • Social data are mainly textual and their analysis is complex. • Approach: a social information discovery system enhanced by sentiment analysis. • Dedicated algorithms for word sense disambiguation and negation handling improve the accuracy of sentiment analysis. • Experimental evidence of efficiency and effectiveness on synthetic and real data. [ABSTRACT FROM AUTHOR]
Copyright of Future Generation Computer Systems is the property of Elsevier B.V. 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: 135493234
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Social information discovery enhanced by sentiment analysis techniques.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Diamantini%2C+Claudia%22">Diamantini, Claudia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> c.diamantini@univpm.it</i><br /><searchLink fieldCode="AR" term="%22Mircoli%2C+Alex%22">Mircoli, Alex</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> a.mircoli@pm.univpm.it</i><br /><searchLink fieldCode="AR" term="%22Potena%2C+Domenico%22">Potena, Domenico</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> d.potena@univpm.it</i><br /><searchLink fieldCode="AR" term="%22Storti%2C+Emanuele%22">Storti, Emanuele</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> e.storti@univpm.it</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Future+Generation+Computer+Systems%22">Future Generation Computer Systems</searchLink>. Jun2019, Vol. 95, p816-828. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Content+analysis%22">Content analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink><br /><searchLink fieldCode="DE" term="%22User-generated+content%22">User-generated content</searchLink><br /><searchLink fieldCode="DE" term="%22Open+innovation%22">Open innovation</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Abstract In recent years, the massive diffusion of social networks has made available a large amount of user-generated content, for the most part in the form of textual data that contain people's thoughts and emotions about a great variety of topics. In order to exploit these publicly available information, in this work we introduce a social information discovery system which elaborates simultaneously over more-than-one social network in an integrated scenario. The system is designed to ensure flexibility and scalability, thus enabling for (near-)real-time analysis even in case of high rates of content's creation and large amounts of heterogeneous data. Furthermore, a noise detection technique ensures a high relevance of analyzed posts/tweets to the domain of interest. We also propose a lexicon-based sentiment analysis algorithm to extract and measure users' opinion, in order to support collaboration and open innovation. Polysemous words and negations are typically challenging for lexicon-based approaches: for this reason, we introduce both a word sense disambiguation algorithm and a negation handling technique. Experiments on several datasets have proven that the combined use of both techniques improves the classification accuracy on 3-class sentiment analysis. Highlights • Social networks provide a large amount of analyzable user-generated content (USG). • Social data are mainly textual and their analysis is complex. • Approach: a social information discovery system enhanced by sentiment analysis. • Dedicated algorithms for word sense disambiguation and negation handling improve the accuracy of sentiment analysis. • Experimental evidence of efficiency and effectiveness on synthetic and real data. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Future Generation Computer Systems is the property of Elsevier B.V. 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=135493234
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.future.2018.01.051
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 816
    Subjects:
      – SubjectFull: Sentiment analysis
        Type: general
      – SubjectFull: Content analysis
        Type: general
      – SubjectFull: Social networks
        Type: general
      – SubjectFull: User-generated content
        Type: general
      – SubjectFull: Open innovation
        Type: general
      – SubjectFull: Information storage & retrieval systems
        Type: general
    Titles:
      – TitleFull: Social information discovery enhanced by sentiment analysis techniques.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Diamantini, Claudia
      – PersonEntity:
          Name:
            NameFull: Mircoli, Alex
      – PersonEntity:
          Name:
            NameFull: Potena, Domenico
      – PersonEntity:
          Name:
            NameFull: Storti, Emanuele
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2019
              Type: published
              Y: 2019
          Identifiers:
            – Type: issn-print
              Value: 0167739X
          Numbering:
            – Type: volume
              Value: 95
          Titles:
            – TitleFull: Future Generation Computer Systems
              Type: main
ResultId 1