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Attention-Based Modality-Gated Networks for Image-Text Sentiment Analysis

Published: 05 July 2020 Publication History

Abstract

Sentiment analysis of social multimedia data has attracted extensive research interest and has been applied to many tasks, such as election prediction and products evaluation. Sentiment analysis of one modality (e.g., text or image) has been broadly studied. However, not much attention has been paid to the sentiment analysis of multimodal data. Different modalities usually have information that is complementary. Thus, it is necessary to learn the overall sentiment by combining the visual content with text description. In this article, we propose a novel method—Attention-Based Modality-Gated Networks (AMGN)—to exploit the correlation between the modalities of images and texts and extract the discriminative features for multimodal sentiment analysis. Specifically, a visual-semantic attention model is proposed to learn attended visual features for each word. To effectively combine the sentiment information on the two modalities of image and text, a modality-gated LSTM is proposed to learn the multimodal features by adaptively selecting the modality that presents stronger sentiment information. Then a semantic self-attention model is proposed to automatically focus on the discriminative features for sentiment classification. Extensive experiments have been conducted on both manually annotated and machine weakly labeled datasets. The results demonstrate the superiority of our approach through comparison with state-of-the-art models.

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 3
August 2020
364 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3409646
Issue’s Table of Contents
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Publication History

Published: 05 July 2020
Online AM: 07 May 2020
Accepted: 01 March 2020
Revised: 01 December 2019
Received: 01 August 2019
Published in TOMM Volume 16, Issue 3

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Author Tags

  1. Sentiment analysis
  2. attention
  3. deep learning
  4. modality-gated
  5. multimodal

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • Science and Technology Program of Guangzhou of China
  • Guangdong Provincial Special Funds for Applied Technology Research and Development and Transformation of Important Scientific and Technological Achieve
  • the National Key R8D Program of China
  • Natural Science Foundation of Guangdong Province, China
  • National Natural Science Foundation of China

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