research-article

Event Graph Neural Network for Opinion Target Classification of Microblog Comments

Authors:

Yantuan XianAuthors Info & Claims

Transactions on Asian and Low-Resource Language Information Processing, Volume 21, Issue 1

Article No.: 17, Pages 1 - 13

https://doi.org/10.1145/3469725

Published: 02 November 2021 Publication History

Abstract

Opinion target classification of microblog comments is one of the most important tasks for public opinion analysis about an event. Due to the high cost of manual labeling, opinion target classification is generally considered as a weak-supervised task. This article attempts to address the opinion target classification of microblog comments through an event graph convolution network (EventGCN) in a weak-supervised manner. Specifically, we take microblog contents and comments as document nodes, and construct an event graph with three typical relationships of event microblogs, including the co-occurrence relationship of event keywords extracted from microblogs, the reply relationship of comments, and the document similarity. Finally, under the supervision of a small number of labels, both word features and comment features can be represented well to complete the classification. The experimental results on two event microblog datasets show that EventGCN can significantly improve the classification performance compared with other baseline models.

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Cited By

Wei KJin LZhang ZGuo ZLi XLiu QFeng W(2024)More Than Syntaxes: Investigating Semantics to Zero-shot Cross-lingual Relation Extraction and Event Argument Role LabellingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358226123:5(1-21)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3582261
Haffar NZrigui M(2023)A Synergistic Bidirectional LSTM and N-gram Multi-channel CNN Approach Based on BERT and FastText for Arabic Event IdentificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/362656822:11(1-27)Online publication date: 20-Nov-2023
https://dl.acm.org/doi/10.1145/3626568

Index Terms

Event Graph Neural Network for Opinion Target Classification of Microblog Comments
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning algorithms

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 21, Issue 1

January 2022

442 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3494068

Editor:
Imed Zitouni
Google, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2021

Accepted: 01 June 2021

Revised: 01 March 2021

Received: 01 October 2020

Published in TALLIP Volume 21, Issue 1

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Cited By

Wei KJin LZhang ZGuo ZLi XLiu QFeng W(2024)More Than Syntaxes: Investigating Semantics to Zero-shot Cross-lingual Relation Extraction and Event Argument Role LabellingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358226123:5(1-21)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3582261
Haffar NZrigui M(2023)A Synergistic Bidirectional LSTM and N-gram Multi-channel CNN Approach Based on BERT and FastText for Arabic Event IdentificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/362656822:11(1-27)Online publication date: 20-Nov-2023
https://dl.acm.org/doi/10.1145/3626568

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