Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ACFR85Z2N" target="_blank" >RIV/00216208:11320/23:CFR85Z2N - isvavai.cz</a>
Result on the web
<a href="https://www.researchsquare.com/article/rs-3208999/latest" target="_blank" >https://www.researchsquare.com/article/rs-3208999/latest</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21203/rs.3.rs-3208999/v1" target="_blank" >10.21203/rs.3.rs-3208999/v1</a>
Alternative languages
Result language
angličtina
Original language name
Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis
Original language description
"Aspect-based sentiment analysis aims to analyze the sentimental tendencies of specific aspect terms in the target sentence. With the continuous development of deep learning technology, graph convolutional networks have been widely applied to sentiment analysis tasks and achieved satisfactory results. However, when constructing graph convolutional networks based on text contents, the model considers the contextual words and their interdependent relationships without distinction."
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
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
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů