Little bad concerns: Using sentiment analysis to assess structural balance in communication networks

Jana Diesner, Craig S. Evans

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present and test a scalable approach for assigning valence to links in unsigned graphs with the ultimate goal of enabling triadic balanced assessment in communication networks. We do this by applying domain-adjusted sentiment analysis to the content of communication data and translating aggregated sentiment scores for information exchanged between network members into link signs. This approach facilitates fast, informed and systematic balance testing (we generate link signs for 166,670 triads in our data); allowing for empirical hypothesis testing and theory building based on current or archival communication data. The proposed technique eliminates the need for manually labeling text data, and overcomes limitations with inferring valence from self-reported or user-generated (meta-) data in situations where historical context and ground truth valence data might be unavailable or limited. We test this approach on corporate email data to complement the large amount of prior work based on social media data and the limited knowledge on sentiment in professional settings. Our results suggest that sentiment is overall slightly positive and emotionality is low, which reflects conventions of language use in a corporate environment. We observe that people draw from (the top of) a smaller pool of positive terms more frequently than from a larger set of negative terms. The ratio of balanced triads (on average about 88%) to unbalanced triads (12%) remains relatively stable despite changes in corporate performance. The labor-intense adjustment of a given lexical resource to some dataset and domain pays off as it generates more empirical evidence with lower variance.

Original languageEnglish (US)
Title of host publicationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
EditorsJian Pei, Jie Tang, Fabrizio Silvestri
PublisherAssociation for Computing Machinery, Inc
Pages342-348
Number of pages7
ISBN (Electronic)9781450338547
DOIs
StatePublished - Aug 25 2015
EventIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France
Duration: Aug 25 2015Aug 28 2015

Publication series

NameProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Other

OtherIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
CountryFrance
CityParis
Period8/25/158/28/15

Keywords

  • Sentiment analysis
  • Signed graphs
  • Structural balance theory
  • Text mining

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

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