Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis

Popis výsledku v původním jazyce

"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."

Název v anglickém jazyce

Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis

Popis výsledku anglicky

"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."

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů