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Open Access

A Multitask Multiview Neural Network for End-to-End Aspect-Based Sentiment Analysis

School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
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Abstract

The aspect-based sentiment analysis (ABSA) consists of two subtasks'aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network (MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.

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Big Data Mining and Analytics
Pages 195-207
Cite this article:
Bie Y, Yang Y. A Multitask Multiview Neural Network for End-to-End Aspect-Based Sentiment Analysis. Big Data Mining and Analytics, 2021, 4(3): 195-207. https://doi.org/10.26599/BDMA.2021.9020003

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Received: 03 January 2021
Accepted: 25 January 2021
Published: 12 May 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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