Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanism

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AYLV8BYFR" target="_blank" >RIV/00216208:11320/25:YLV8BYFR - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169341824&doi=10.1007%2fs10844-023-00811-2&partnerID=40&md5=da9160014c49c7606a9df31667672baf" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169341824&doi=10.1007%2fs10844-023-00811-2&partnerID=40&md5=da9160014c49c7606a9df31667672baf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10844-023-00811-2" target="_blank" >10.1007/s10844-023-00811-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanism

Popis výsledku v původním jazyce

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years. It involves several subtasks for extracting one or more sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. In this paper, we propose two novel approaches to addressing different ABSA subtasks. Firstly, we introduce a Dependency-Attention GCN-based Aspect Opinion Extractor (DAG-AOE) for the Aspect-Opinion Pair Extraction (AOPE) task. DAG-AOE employs an improved graph convolutional network to extract syntactic structure information from text sequences, thereby effectively identifying aspect-opinion pairs in sentences. Secondly, we propose a Mutual Assistance Mechanism-based Category Sentiment Classifier (MAM-CSC) that utilizes the results of DAG-AOE to address the Aspect Sentiment Quad Prediction (ASQP) task. MAM-CSC leverages the semantic relationships between words in a sentence and addresses the two independent classification tasks through a mutual assistance approach. We conduct extensive experiments on benchmark datasets, and the experimental results demonstrate that our models have demonstrated a significant improvement over all baseline methods. Specifically, our models achieved the highest improvement of 1.4% F1 score over the baseline on the AOPE task and 1.5% F1 score on the ASQP task. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

Název v anglickém jazyce

Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanism

Popis výsledku anglicky

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years. It involves several subtasks for extracting one or more sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. In this paper, we propose two novel approaches to addressing different ABSA subtasks. Firstly, we introduce a Dependency-Attention GCN-based Aspect Opinion Extractor (DAG-AOE) for the Aspect-Opinion Pair Extraction (AOPE) task. DAG-AOE employs an improved graph convolutional network to extract syntactic structure information from text sequences, thereby effectively identifying aspect-opinion pairs in sentences. Secondly, we propose a Mutual Assistance Mechanism-based Category Sentiment Classifier (MAM-CSC) that utilizes the results of DAG-AOE to address the Aspect Sentiment Quad Prediction (ASQP) task. MAM-CSC leverages the semantic relationships between words in a sentence and addresses the two independent classification tasks through a mutual assistance approach. We conduct extensive experiments on benchmark datasets, and the experimental results demonstrate that our models have demonstrated a significant improvement over all baseline methods. Specifically, our models achieved the highest improvement of 1.4% F1 score over the baseline on the AOPE task and 1.5% F1 score on the ASQP task. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Intelligent Information Systems

ISSN

0925-9902

e-ISSN

—

Svazek periodika

62

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

27

Strana od-do

163-189

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85169341824