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Deep Interactive Memory Network for Aspect-Level Sentiment Analysis

Published:01 December 2020Publication History
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Editorial Notes

The editors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on February 9, 2021. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

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Abstract

The goal of aspect-level sentiment analysis is to identify the sentiment polarity of a specific opinion target expressed; it is a fine-grained sentiment analysis task. Most of the existing works study how to better use the target information to model the sentence without using the interactive information between the sentence and target. In this article, we argue that the prediction of aspect-level sentiment polarity depends on both context and target. First, we propose a new model based on LSTM and the attention mechanism to predict the sentiment of each target in the review, the matrix-interactive attention network (M-IAN) that models target and context, respectively. M-IAN use an attention matrix to learn the interactive attention of context and target and generates the final representations of target and context. Then we introduce two gate networks based on M-IAN to build a deep interactive memory network to capture multiple interactions of target and context. The deep interactive memory network can excellently formulate specific memory for different targets, which is helpful in sentiment analysis. The experimental results of Restaurant and Laptop datasets of SemEval 2014 validate the effectiveness of our model.

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            cover image ACM Transactions on Asian and Low-Resource Language Information Processing
            ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 1
            Special issue on Deep Learning for Low-Resource Natural Language Processing, Part 1 and Regular Papers
            January 2021
            332 pages
            ISSN:2375-4699
            EISSN:2375-4702
            DOI:10.1145/3439335
            Issue’s Table of Contents

            Copyright © 2020 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 1 December 2020
            • Accepted: 1 May 2020
            • Revised: 1 April 2020
            • Received: 1 February 2020
            Published in tallip Volume 20, Issue 1

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