TY - GEN
T1 - Interactive Fusion Network with Recurrent Attention for Multimodal Aspect-based Sentiment Analysis
AU - Wang, Jun
AU - Wang, Qianlong
AU - Wen, Zhiyuan
AU - Liang, Xingwei
AU - Xu, Ruifeng
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The goal of multimodal aspect-based sentiment analysis is to comprehensively utilize data from different modalities (e.g.,, text and image) to identify aspect-specific sentiment polarity. Existing works have proposed many methods for fusing text and image information and achie-ved satisfactory results. However, they fail to filter noise in the image information and ignore the progressive learning process of sentiment features. To solve these problems, we propose an interactive fusion network with recurrent attention. Specifically, we first use two encoders to encode text and image data, respectively. Then we use the attention mechanism to obtain the semantic information of the image at the token level. Next, we employ GRU to filter out the noise in the image and fuse information from different modalities. Finally, we design a decoder with recurrent attention to progressively learn aspect-specific sentiment features for classification. The results on two Twitter datasets show that our method outperforms all baselines.
AB - The goal of multimodal aspect-based sentiment analysis is to comprehensively utilize data from different modalities (e.g.,, text and image) to identify aspect-specific sentiment polarity. Existing works have proposed many methods for fusing text and image information and achie-ved satisfactory results. However, they fail to filter noise in the image information and ignore the progressive learning process of sentiment features. To solve these problems, we propose an interactive fusion network with recurrent attention. Specifically, we first use two encoders to encode text and image data, respectively. Then we use the attention mechanism to obtain the semantic information of the image at the token level. Next, we employ GRU to filter out the noise in the image and fuse information from different modalities. Finally, we design a decoder with recurrent attention to progressively learn aspect-specific sentiment features for classification. The results on two Twitter datasets show that our method outperforms all baselines.
KW - Attention mechanism
KW - Multimodal aspect-based sentiment analysis
KW - Progressively learning
UR - https://www.scopus.com/pages/publications/85145262433
U2 - 10.1007/978-3-031-20503-3_24
DO - 10.1007/978-3-031-20503-3_24
M3 - 会议稿件
AN - SCOPUS:85145262433
SN - 9783031205026
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 298
EP - 309
BT - Artificial Intelligence - Second CAAI International Conference, CICAI 2022, Revised Selected Papers
A2 - Fang, Lu
A2 - Povey, Daniel
A2 - Zhai, Guangtao
A2 - Mei, Tao
A2 - Wang, Ruiping
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd CAAI International Conference on Artificial Intelligence, CICAI 2022
Y2 - 27 August 2022 through 28 August 2022
ER -