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Semi-Automatic Approaches for Exploiting Shifter Patterns in Domain-Specific Sentiment Analysis

Mathematics, ISSN: 2227-7390, Vol: 10, Issue: 18
2022
  • 4
    Citations
  • 0
    Usage
  • 15
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    4
    • Citation Indexes
      4
  • Captures
    15

Article Description

This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction.

Bibliographic Details

Pavel Brazdil; Shamsuddeen H. Muhammad; João Cordeiro; Fátima Oliveira; Fátima Silva; Purificação Silvano; António Leal

MDPI AG

Computer Science; Mathematics; Engineering

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