Sentiment Analysis of Social Network Data for Cold-Start Relief in Recommender Systems

  • Felipe G. Contratres
  • Solange N. Alves-Souza
  • Lucia Vilela Leite Filgueiras
  • Luiz S. DeSouza
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

Recommender systems have been used in e-commerce to increase conversion due to matching product offer and consumer preferences. Cold-start is the situation of a new user about whom there is no information to make suitable recommendations. Texts published by the user in social networks are a good source of information to reduce the cold-start issue. However, the valence of the emotion in a text must be considered in the recommendation so that no product is recommended based on a negative opinion. This paper proposes a recommendation process that includes sentiment analysis to textual data extracted from Facebook and Twitter and present results of an experiment in which this algorithm is used to reduce the cold-start issue.

Keywords

Social network Cold-Start Recommender Systems Sentimental analysis Social media 

Notes

Acknowledgments

Authors are grateful for the support given by São Paulo Research Foundation (FAPESP). Grant #2014/04851-8, São Paulo Research Foundation (FAPESP).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Felipe G. Contratres
    • 1
  • Solange N. Alves-Souza
    • 1
  • Lucia Vilela Leite Filgueiras
    • 1
  • Luiz S. DeSouza
    • 2
  1. 1.Universidade de São Paulo - Escola PolitécnicaSão PauloBrazil
  2. 2.Faculdade de TecnologiaSão PauloBrazil

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