Semi-Automatic Approaches for Exploiting Shifter Patterns in Domain-Specific Sentiment Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A49KCB4RR" target="_blank" >RIV/00216208:11320/22:49KCB4RR - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2227-7390/10/18/3232" target="_blank" >https://www.mdpi.com/2227-7390/10/18/3232</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/math10183232" target="_blank" >10.3390/math10183232</a>
Alternative languages
Result language
angličtina
Original language name
Semi-Automatic Approaches for Exploiting Shifter Patterns in Domain-Specific Sentiment Analysis
Original language 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Mathematics [online]
ISSN
2227-7390
e-ISSN
2227-7390
Volume of the periodical
10
Issue of the periodical within the volume
18
Country of publishing house
CH - SWITZERLAND
Number of pages
24
Pages from-to
1-24
UT code for WoS article
000857610000001
EID of the result in the Scopus database
2-s2.0-85138638378