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A network-based matching design for text mining of hyper-polarised online reviews

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  • Cantone, Giulio Giacomo
  • Tomaselli, Venera

Abstract

Online reviews provide users with the opportunity to rate various types of items such as movies, music, and video games using a combination of numeric scores and textual comments. The study proposes a novel method that combines network modeling with statistical matching to estimate the unbiased association between words and hyper-polarized items in online reviews. The application of this method to a sample of 40,665 items from the website Metacritic detects 218 hyper-polarized items; these are matched with an equal number of items using 8 covariates of item quality and network centrality. Application of the method reveals an unbiased association between hyper-polarization and semantics indicating reactive social action in online reviews, especially related to controversial political issues in the USA.

Suggested Citation

  • Cantone, Giulio Giacomo & Tomaselli, Venera, 2023. "A network-based matching design for text mining of hyper-polarised online reviews," SocArXiv v7u3h_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:v7u3h_v1
    DOI: 10.31219/osf.io/v7u3h_v1
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    References listed on IDEAS

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