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Political marketing with data analytics

Author

Listed:
  • Dennis F. X. Mathaisel

    (Babson College)

  • Clare L. Comm

    (University of Massachusetts Lowell)

Abstract

Social media played a significant role in past Presidential elections, and it is very likely that this form of communication will continue to influence political campaigns. Can analytics uncover the linguistic “plot arcs” and resulting sentiment or emotion in political text? This paper examines how natural language processing (NLP) and data visualization tools and methods in analytics can play a key role in marketing political candidates. Using publicly available text messages, the authors employ NLP techniques to transform the text observations from the past campaigns of Hillary Clinton, Barack Obama, and Donald Trump into a linguistic “corpus” and story arc visualizations. The methodology includes the use of Syuzhet and Latent Dirichlet Allocation (LDA) models. The resulting data visualizations reveal the story arcs associated with the candidate’s communications, and they provide a means to analyze the unbiased political sentiment or hidden emotion in the text. In an analysis of the results, the authors found distinctly different story arcs and vocabulary usage among the three Presidential candidates. The contribution to the literature is a methodology for extracting the story and the resulting sentiment from text messages for marketing campaigns. The authors suggest that the techniques used in this paper can assist future research on marketing other products or services that utilize computer-mediated communications.

Suggested Citation

  • Dennis F. X. Mathaisel & Clare L. Comm, 2021. "Political marketing with data analytics," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(1), pages 56-64, March.
    • Handle: RePEc:pal:jmarka:v:9:y:2021:i:1:d:10.1057_s41270-020-00097-1
    DOI: 10.1057/s41270-020-00097-1
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    References listed on IDEAS

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    1. Gil Appel & Lauren Grewal & Rhonda Hadi & Andrew T. Stephen, 2020. "The future of social media in marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 79-95, January.
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    Cited by:

    1. Djonata Schiessl & Helison Bertoli Alves Dias & José Carlos Korelo, 2022. "Artificial intelligence in marketing: a network analysis and future agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 207-218, September.
    2. Jorge Iván Pérez Rave & Gloria Patricia Jaramillo Álvarez & Juan Carlos Correa Morales, 2023. "Psycho-managerial text mining (PMTM): a framework for developing and validating psychological/managerial constructs from a theory/text-driven approach," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 777-808, December.
    3. Jorge Iván Pérez Rave & Gloria Patricia Jaramillo Álvarez & Juan Carlos Correa Morales, 2022. "Multi-criteria decision-making leveraged by text analytics and interviews with strategists," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(1), pages 30-49, March.

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