skip to main content
10.1145/2808797.2809403acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
short-paper

Little Bad Concerns: Using Sentiment Analysis to Assess Structural Balance in Communication Networks

Published: 25 August 2015 Publication History

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.

References

[1]
D. Cartwright, and F. Harary, "Structural balance: a generalization of Heider's theory," Psychological Review, vol. 63, no. 5, pp. 277--293, 1956.
[2]
F. Heider, "Attitudes and cognitive organization," The Journal of Psychology, vol. 21, no. 1, pp. 107--112, 1946.
[3]
J. A. Davis, "Structural balance, mechanical solidarity, and interpersonal relations," American Journal of Sociology, pp. 444--462, 1963.
[4]
T. Antal, P. L. Krapivsky, and S. Redner, "Social balance on networks: The dynamics of friendship and enmity," Physica D: Nonlinear Phenomena, vol. 224, no. 1, pp. 130--136, 2006.
[5]
J. Leskovec, D. Huttenlocher, and J. Kleinberg, "Signed networks in social media," In Proceedings of SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA, ACM, pp. 1361--1370, 2010.
[6]
J. Yang, L. Adamic, M. Ackerman, Z. Wen, and C.-Y. Lin, "The way I talk to you: Sentiment expression in an organizational context," In Proceedings of SIGCHI Conference on Human Factors in Computing Systems, Austin, TX, USA, ACM, pp. 551--554, 2012.
[7]
J. G. Shanahan, Y. Qu, and J. M. Wiebe, Computing attitude and affect in text: theory and applications: Springer, 2006.
[8]
S. Brody, and N. Diakopoulos, "Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs," In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Edinburgh, UK, Association for Computational Linguistics, pp. 562--570, 2011.
[9]
B. Pang, and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1--135, 2008.
[10]
A. Kennedy, and D. Inkpen, "Sentiment classification of movie reviews using contextual valence shifters," Computational Intelligence, vol. 22, no. 2, pp. 110--125, 2006.
[11]
A. Hassan, A. Abu-Jbara, and D. Radev, "Extracting signed social networks from text," In Proceedings of Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-7), Jeju Island, Republic of Korea, Association for Computational Linguistics, pp. 6--14, 2012.
[12]
A. Hassan, A. Abu-Jbara, and D. Radev, "Detecting subgroups in online discussions by modeling positive and negative relations among participants," In Proceedings of Joint Conference on Empirical Methods in Natural Language Processing (EMNLP) and Computational Natural Language Learning Jeju Island, Korea, Association for Computational Linguistics, pp. 59--70, 2012.
[13]
J. Diesner, C. Evans, and J. Kim, "Impact of entity disambiguation errors on social network properties," in Proceedings of International AAAI Conference on Web and Social Media (ICWSM), Oxford, UK, 2015.
[14]
J. Diesner, T. L. Frantz, and K. M. Carley, "Communication Networks from the Enron Email Corpus. "It's Always About the People. Enron is no Different"," Computational & Mathematical Organization Theory, vol. 11, no. 3, pp. 201--228, 2005.
[15]
L. Fox, Enron: The rise and fall, Hoboken: John Wiley & Sons Inc, 2003.
[16]
P. C. Fusaro, and R. M. Miller, What went wrong at Enron: Everyone's guide to the largest bankruptcy in US history, Hoboken: Wiley, 2002.
[17]
D. Klein, and C. D. Manning, "Accurate unlexicalized parsing," In Proceedings of 41st Annual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA, Association for Computational Linguistics, pp. 423--430, 2003.
[18]
T. Wilson, J. Wiebe, and P. Hoffmann, "Recognizing contextual polarity in phrase-level sentiment analysis," In Proceedings of Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT-EMNLP), Vancouver, BC, Canada, Association for Computational Linguistics, pp. 347--354, 2005.
[19]
J. Abello, P. Broadwell, and T. R. Tangherlini, "Computational folkloristics," Communications of the ACM, vol. 55, no. 7, pp. 60--70, 2012.

Cited By

View all
  • (2024)Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directionsIntelligent Systems in Accounting, Finance and Management10.1002/isaf.154931:1Online publication date: 25-Feb-2024
  • (2023)Enhancing structural balance theory and measurement to analyze signed digraphs of real-world social networksFrontiers in Human Dynamics10.3389/fhumd.2022.10283934Online publication date: 16-Feb-2023
  • (2022)Information Extraction from Social MediaProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557503(5148-5151)Online publication date: 17-Oct-2022
  • Show More Cited By
  1. Little Bad Concerns: Using Sentiment Analysis to Assess Structural Balance in Communication Networks

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015
      August 2015
      835 pages
      ISBN:9781450338547
      DOI:10.1145/2808797
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 25 August 2015

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Structural balance theory
      2. sentiment analysis
      3. signed graphs
      4. text mining

      Qualifiers

      • Short-paper
      • Research
      • Refereed limited

      Conference

      ASONAM '15
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 116 of 549 submissions, 21%

      Upcoming Conference

      KDD '25

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)12
      • Downloads (Last 6 weeks)3
      Reflects downloads up to 19 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directionsIntelligent Systems in Accounting, Finance and Management10.1002/isaf.154931:1Online publication date: 25-Feb-2024
      • (2023)Enhancing structural balance theory and measurement to analyze signed digraphs of real-world social networksFrontiers in Human Dynamics10.3389/fhumd.2022.10283934Online publication date: 16-Feb-2023
      • (2022)Information Extraction from Social MediaProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557503(5148-5151)Online publication date: 17-Oct-2022
      • (2021)Enhancing Theory-Informed Dictionary Approaches with “Glass-box” Machine Learning: The Case of Integrative Complexity in Social Media CommentsCommunication Methods and Measures10.1080/19312458.2021.199991316:4(303-320)Online publication date: 17-Nov-2021
      • (2021)Balance and fragmentation in societies with homophily and social balanceScientific Reports10.1038/s41598-021-96065-511:1Online publication date: 25-Aug-2021
      • (2021)Which Group Do You Belong To? Sentiment-Based PageRank to Measure Formal and Informal Influence of Nodes in NetworksComplex Networks & Their Applications IX10.1007/978-3-030-65351-4_50(623-636)Online publication date: 5-Jan-2021
      • (2020)Multilevel structural evaluation of signed directed social networks based on balance theoryScientific Reports10.1038/s41598-020-71838-610:1Online publication date: 17-Sep-2020
      • (2020)Adjusting Chatbot Conversation to User Personality and MoodArtificial Intelligence for Customer Relationship Management10.1007/978-3-030-61641-0_3(93-127)Online publication date: 24-Dec-2020
      • (2019)Towards the Recognition of the Emotions of People with Visual Disabilities through Brain–Computer InterfacesSensors10.3390/s1911262019:11(2620)Online publication date: 9-Jun-2019
      • (2016)Exploring public sentiments for livable places based on a crowd-calibrated sentiment analysis mechanismProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3192424.3192555(693-700)Online publication date: 18-Aug-2016
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media