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

Pavel Brazdil (), Shamsuddeen H. Muhammad (), Fátima Oliveira, João Cordeiro, Fátima Silva, Purificação Silvano and António Leal
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Pavel Brazdil: FEP, University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal
Shamsuddeen H. Muhammad: INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Fátima Oliveira: FLUP/CLUP, University of Porto, Via Panorâmica, s/n, 4150-564 Porto, Portugal
João Cordeiro: INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Fátima Silva: FLUP/CLUP, University of Porto, Via Panorâmica, s/n, 4150-564 Porto, Portugal
Purificação Silvano: FLUP/CLUP, University of Porto, Via Panorâmica, s/n, 4150-564 Porto, Portugal
António Leal: FLUP/CLUP, University of Porto, Via Panorâmica, s/n, 4150-564 Porto, Portugal

Mathematics, 2022, vol. 10, issue 18, 1-24

Abstract: 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.

Keywords: sentiment analysis; automatic lexicon generation; domain-specific lexicon; contextual shifters; intensification, attenuation and negation; deep learning in sentiment analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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