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Market Segmentation Using Data Mining Techniques in Social Networks

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Computer Science – CACIC 2018 (CACIC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 995))

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Abstract

Social networks have gained great popularity during the last decade, due to the advance of new technologies and people’s growing interest in generating content and sharing it with their contacts. This makes data generated in social networks grow exponentially over time.

These generated data contain information that can be analyzed, in order to discover patterns that can be of help in multiple disciplines. Marketing is one of these disciplines that is closely linked to understanding people’s behaviors, tendencies and tastes. The aim of this study is to apply data mining (DM) to discover patterns in data coming from social networks. Obtaining patterns will enable to carry out different types of segmentations to help the marketing professionals direct their campaigns.

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Notes

  1. 1.

    Pytweet is a pythonic library that provides a simple interface for the Twitter API. Values ​​are normalized in Python types.

  2. 2.

    “Application Programming Interface”. In computer programming, an application programming interface is a set of subroutine definitions, protocols, and tools for creating software applications.

  3. 3.

    It is an open source distribution of Python and R programming languages ​​for large-scale data processing, predictive analysis and scientific computing, which aims to simplify package management and deployment.

    .

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Author information

Authors and Affiliations

  1. School of Information Systems, Universidad Argentina John F. Kennedy, Bartolomé Mitre 1411, Ciudad Autónoma de Buenos Aires, C1037ABA, Argentina

    Eduin Olarte & Marisa Panizzi

  2. School of Computer Science, Computer Science Research Institute LIDI (LIDI-III), National University of La Plata, La Plata, Argentina

    Rodolfo Bertone

Authors
  1. Eduin Olarte
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  2. Marisa Panizzi
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  3. Rodolfo Bertone
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Corresponding author

Correspondence to Eduin Olarte .

Editor information

Editors and Affiliations

  1. National University of La Plata, La Plata, Argentina

    Patricia Pesado

  2. National University of Buenos Aires Center, Buenos Aires, Argentina

    Claudio Aciti

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Olarte, E., Panizzi, M., Bertone, R. (2019). Market Segmentation Using Data Mining Techniques in Social Networks. In: Pesado, P., Aciti, C. (eds) Computer Science – CACIC 2018. CACIC 2018. Communications in Computer and Information Science, vol 995. Springer, Cham. https://doi.org/10.1007/978-3-030-20787-8_16

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  • DOI: https://doi.org/10.1007/978-3-030-20787-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20786-1

  • Online ISBN: 978-3-030-20787-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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