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A Pipeline for Measuring Brand Loyalty Through Social Media Mining

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SOFSEM 2021: Theory and Practice of Computer Science (SOFSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12607))

Abstract

Enhancing customer relationships through social media is an area of high relevance for companies. To this aim, Social Business Intelligence (SBI) plays a crucial role by supporting companies in combining corporate data with user-generated content, usually available as textual clips on social media. Unfortunately, SBI research is often constrained by the lack of publicly-available, real-world data for experimental activities. In this paper, we describe our experience in extracting social data and processing them through an enrichment pipeline for brand analysis. As a first step, we collect texts from social media and we annotate them based on predefined metrics for brand analysis, using features such as sentiment and geolocation. Annotations rely on various learning and natural language processing approaches, including deep learning and geographical ontologies. Structured data obtained from the annotation process are then stored in a distributed data warehouse for further analysis. Preliminary results, obtained from the analysis of three well known ICT brands, using data gathered from Twitter, news portals, and Amazon product reviews, show that different evaluation metrics can lead to different outcomes, indicating that no single metric is dominant for all brand analysis use cases.

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Notes

  1. 1.

    http://cloud.google.com/natural-language/docs.

  2. 2.

    http://scikit-learn.org/stable/.

  3. 3.

    https://github.com/zfz/twitter_corpus/blob/master/full-corpus.csv.

  4. 4.

    http://www.kaggle.com/kazanova/sentiment140.

  5. 5.

    http://keras.io/.

  6. 6.

    http://code.google.com/archive/p/geoloc-kde/.

  7. 7.

    http://www.geosparql.org/.

  8. 8.

    http://www.geonames.org/ontology/.

  9. 9.

    http://wiki.osmfoundation.org/.

  10. 10.

    http://cloud.google.com/natural-language/docs/analyzing-entities.

  11. 11.

    http://wiki.dbpedia.org/.

  12. 12.

    https://newsapi.org/.

  13. 13.

    https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data

  14. 14.

    https://pypi.org/project/yahoo-finance/.

  15. 15.

    https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html.

  16. 16.

    https://spark.apache.org/sql/.

  17. 17.

    http://www.tableau.com.

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Author information

Authors and Affiliations

  1. Chalmers University of Technology, Gothenburg, Sweden

    Hazem Samoaa

  2. University of Genoa, Genoa, Italy

    Barbara Catania

Authors
  1. Hazem Samoaa
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  2. Barbara Catania
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Corresponding author

Correspondence to Hazem Samoaa .

Editor information

Editors and Affiliations

  1. Charles University, Prague, Czech Republic

    Tomáš Bureš

  2. University of Bergamo, Bergamo, Italy

    Riccardo Dondi

  3. Free University of Bozen-Bolzano, Bolzano, Italy

    Johann Gamper

  4. University of Genoa, Genoa, Italy

    Giovanna Guerrini

  5. University of Wroclaw, Wroclaw, Poland

    Tomasz Jurdziński

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Claus Pahl

  7. University Paris Dauphine, Paris, France

    Florian Sikora

  8. University of Liverpool, Liverpool, UK

    Prudence W.H. Wong

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Samoaa, H., Catania, B. (2021). A Pipeline for Measuring Brand Loyalty Through Social Media Mining. In: Bureš, T., et al. SOFSEM 2021: Theory and Practice of Computer Science. SOFSEM 2021. Lecture Notes in Computer Science(), vol 12607. Springer, Cham. https://doi.org/10.1007/978-3-030-67731-2_36

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  • DOI: https://doi.org/10.1007/978-3-030-67731-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67730-5

  • Online ISBN: 978-3-030-67731-2

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