在线评论的静态多模态情感分析

doi:10.3969/j.issn.0255-8297.2022.01.003

Abstract

Abstract: This paper proposes a static multi-modal sentiment classification model based on Pre-LN Transformer. This model firstly extracts semantic features from reviews using the encoder in Pre-LN Transformer structure, in which the multi-head self-attention mechanism allows the model to learn relevant emotional information in different subspaces. Then our model extracts the image features according to ResNet in the reviews. On the basis of feature level fusion, the visual attention mechanism guides the sentiment classification of text and realizes the static multimodal sentiment analysis of online reviews. Experimental results show that our model improves the performance by 1.34% and 1.10% in evaluation accuracy than BiGRU-mVGG and Trans-mVGG on Yelp datasets, which verifies the effectiveness and feasibility of the proposed model.

Key words: sentiment analysis, static multimodal, online reviews, visual aspect attention

CLC Number:

TP391

WANG Kaixin, XU Xiujuan, LIU Yu, ZHAO Zhehuan, ZHAO Xiaowei. Static Multimodal Sentiment Analysis of Online Reviews[J]. Journal of Applied Sciences, 2022, 40(1): 25-35.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2022.01.003

https://www.jas.shu.edu.cn/EN/Y2022/V40/I1/25

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Static Multimodal Sentiment Analysis of Online Reviews

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