skip to main content
10.1145/3472714.3473624acmconferencesArticle/Chapter ViewAbstractPublication PagesdocConference Proceedingsconference-collections
research-article
Open Access

Sentiment Analysis of Portuguese Political Parties Communication

Published:12 October 2021Publication History

ABSTRACT

Political communication in social media has gained increasing importance in the last years. In this study, we analyze the political parties’ communication on Twitter and understand the sentiment of their communication. First by identifying their communication performance regarding the daily number of tweets, favorite tweets, number of retweets per day and per political party. We present a sentiment analysis by the political party using tweets data. In this study, we propose an explanatory model with the main drivers of retweets. To conduct this study, our approach used data analysis and machine learning techniques methods. Results indicate the main determinants that influence future retweets of political posts globally. Here we present a comparison of the communication content between tweets posts and the political parties’ programs available on their institutional websites. We identify the similarities between tweets and formal programs per party and among all parties. This study contributes to analyze the coherence and effectiveness of the political parties’ communication.

References

  1. P. Ekman. 1992. “An argument for basic emotions,” Cognition & emotion, vol. 6, no. 3–4, pp. 169–200. DOI: https://doi.org/10.1080/02699939208411068Google ScholarGoogle ScholarCross RefCross Ref
  2. D. A. Sauter, F. Eisner, P. Ekman, and S. K. Scott. 2010. “Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations,” Proceedings of the National Academy of Sciences, vol. 107, no. 6, pp. 2408–2412. https://doi.org/10.1073/pnas.0908239106Google ScholarGoogle ScholarCross RefCross Ref
  3. R. Plutchik. 1980. “A general psychoevolutionary theory of emotion,” in Theories of emotion, Elsevier, pp. 3–33.Google ScholarGoogle Scholar
  4. S. M. Mohammad and P. D. Turney. 2013. “Crowdsourcing a word–emotion association lexicon,” Computational intelligence, vol. 29, no. 3, pp. 436–465.Google ScholarGoogle ScholarCross RefCross Ref
  5. C. Hutto and E. Gilbert. 2014. “Vader: A parsimonious rule-based model for sentiment analysis of social media text,” in Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, no. 1.Google ScholarGoogle Scholar
  6. S. Aral and D. Eckles. 2019. “Protecting elections from social media manipulation,” Science, vol. 365, no. 6456, pp. 858–861.Google ScholarGoogle ScholarCross RefCross Ref
  7. L. M. Kruse, D. R. Norris, and J. R. Flinchum. 2018. “Social media as a public sphere? Politics on social media,” The Sociological Quarterly, vol. 59, no. 1, pp. 62–84.Google ScholarGoogle ScholarCross RefCross Ref
  8. Mandal, K. Ghosh, S. Ghosh, and S. Mandal. 2021. “Unsupervised approaches for measuring textual similarity between legal court case reports,” Artificial Intelligence and Law, pp. 1–35.Google ScholarGoogle Scholar
  9. K. W. Boyack 2011. “Clustering more than two million biomedical publications: Comparing the accuracies of nine text-based similarity approaches,” PloS one, vol. 6, no. 3, p. e18029.Google ScholarGoogle ScholarCross RefCross Ref
  10. D. J. MacKay. 1992. “Bayesian interpolation,” Neural computation, vol. 4, no. 3, pp. 415–447.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. E. Tipping. 2001. “Sparse Bayesian learning and the relevance vector machine,” Journal of machine learning research, vol. 1, no. Jun, pp. 211–244.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D. P. Kingma and J. Ba. 2014. “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980.Google ScholarGoogle Scholar
  13. C. J. Vargo, L. Guo, M. McCombs, and D. L. Shaw. 2014. “Network issue agendas on Twitter during the 2012 US presidential election,” Journal of Communication, vol. 64, no. 2, pp. 296–316.Google ScholarGoogle ScholarCross RefCross Ref
  14. B. Joyce and J. Deng. 2017. “Sentiment analysis of tweets for the 2016 US presidential election,” in 2017 ieee mit undergraduate research technology conference (urtc). pp. 1–4.Google ScholarGoogle Scholar
  15. D. A. Pereira. 2021. “A survey of sentiment analysis in the Portuguese language,” Artificial Intelligence Review, vol. 54, no. 2, pp. 1087–1115.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Aral. 2020. The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health–and How We Must Adapt. Currency.Google ScholarGoogle Scholar
  17. M. D. Conover, B. Gonçalves, J. Ratkiewicz, A. Flammini, and F. Menczer. 2011.“Predicting the political alignment of twitter users,” in 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing. pp. 192–199.Google ScholarGoogle Scholar
  18. D. Hagar. 2015. “# vote4me: The impact of Twitter on municipal campaign success,” in Proceedings of the 2015 International Conference on Social Media & Society. pp. 1–7.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Q. Zhang, Y. Gong, J. Wu, H. Huang, and X. Huang. 2016. “Retweet prediction with attention-based deep neural network,” in Proceedings of the 25th ACM international on conference on information and knowledge management. pp. 75–84.Google ScholarGoogle Scholar
  20. H.-K. Peng, J. Zhu, D. Piao, R. Yan, and Y. Zhang. 2011. “Retweet modeling using conditional random fields,” in 2011 IEEE 11th International Conference on Data Mining Workshops pp. 336–343.Google ScholarGoogle Scholar
  21. J. Chen, M. S. Hossain, and H. Zhang. 2020. “Analyzing the sentiment correlation between regular tweets and retweets,” Social Network Analysis and Mining, vol. 10, no. 1, pp. 1–9.Google ScholarGoogle ScholarCross RefCross Ref
  22. M. Aparicio and C. J. Costa. 2012. “Collaborative systems: characteristics and features,” in Proceedings of the 30th ACM international conference on Design of communication. pp. 141–146. DOI: https://doi.org/doi.org/10.1145/2379057.2379087Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. S. Aparicio, J. T. Aparicio, and C. J. Costa. 2019. “Data Science and AI: trends analysis,” in 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). pp. 1–6. DOI: https://doi.org/10.23919/CISTI.2019.8760820Google ScholarGoogle Scholar
  24. C. J. Costa and J. T. Aparicio. 2020. “POST-DS: A Methodology to Boost Data Science,” in 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) pp. 1–6. DOI: https://doi.org/10.23919/CISTI49556.2020.9140932Google ScholarGoogle Scholar
  25. C. J. Costa and M. Aparicio. 2013. “Social networks: intentions and usage,” in Proceedings of the 2013 International Conference on Information Systems and Design of Communication. pp. 101–107. DOI: https://doi.org/10.1145/2503859.2503875Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. C. J. Costa, M. Aparício, and A. S. Braga. 2012. ‘Design of communication: a review of theories and models’, New York, NY, USA, 2012, pp. 15–19. doi: 10.1145/2311917.2311921.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. J. T. Aparicio, J. Salema de Sequeira and C. J. Costa. 2021. "Emotion analysis of Portuguese Political Parties Communication over the covid-19 Pandemic," 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), 2021, pp. 1-6, DOI: https://doi.org/10.23919/CISTI52073.2021.9476557Google ScholarGoogle Scholar
  28. M. Aparicio, & C. Costa. 2001. A First step Toward E-politics: A Better Informed Citizen, in S. Bjornestad, R. Moe, A. Morch, A. Opdahl (Eds.), Proceedings of IRIS24: The 24th Information Systems Research Seminar in Scandinavia Vol. 1; 2001, pp.23-34, ISBN 82-7354072-3.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    SIGDOC '21: Proceedings of the 39th ACM International Conference on Design of Communication
    October 2021
    402 pages
    ISBN:9781450386289
    DOI:10.1145/3472714

    Copyright © 2021 ACM

    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]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 12 October 2021

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate355of582submissions,61%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format