Sentiment Propagation for Predicting Reputation Polarity

  • Anastasia Giachanou
  • Julio Gonzalo
  • Ida Mele
  • Fabio Crestani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

One of the core tasks of Online Reputation Monitoring is to determine whether a text mentioning the entity of interest has positive or negative implications for its reputation. A challenging aspect of the task is that many texts are polar facts, i.e. they do not convey sentiment but they do have reputational implications (e.g. A Samsung smartphone exploded during flight has negative implications for the reputation of Samsung). In this paper we explore the hypothesis that, in order to determine the reputation polarity of factual information, we can propagate sentiment from sentiment-bearing texts to factual texts that discuss the same issue. We test two approaches that implement such hypothesis: the first one is to directly propagate sentiment to similar texts, and the second one is to augment the polarity lexicon. Our results (i) confirm our propagation hypothesis, with improvements of up to 43% in weakly supervised settings and up to 59% with fully supervised methods; and (ii) indicate that building domain-specific polarity lexicons is a cost-effective strategy.

Keywords

Reputation polarity Sentiment propagation 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anastasia Giachanou
    • 1
  • Julio Gonzalo
    • 2
  • Ida Mele
    • 1
  • Fabio Crestani
    • 1
  1. 1.Faculty of InformaticsUniversità della Svizzera Italiana (USI)LuganoSwitzerland
  2. 2.UNED NLP & IR GroupMadridSpain

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