MSFNet: modality smoothing fusion network for multimodal aspect-based sentiment analysis
Multimodal aspect-based sentiment classification (MABSC) aims to determine the sentiment polarity of a given aspect in a sentence by combining text and image information. Although the text and the corresponding image in a sample are associated with aspect information, their features are represented...
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Veröffentlicht in: | Frontiers in physics 2023-05, Vol.11 |
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Sprache: | eng |
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Zusammenfassung: | Multimodal aspect-based sentiment classification (MABSC) aims to determine the sentiment polarity of a given aspect in a sentence by combining text and image information. Although the text and the corresponding image in a sample are associated with aspect information, their features are represented in distinct semantic spaces, creating a substantial semantic gap. Previous research focused primarily on identifying and fusing aspect-level sentiment expressions of different modalities while ignoring their semantic gap. To this end, we propose a novel aspect-based sentiment analysis model named modality smoothing fusion network (MSFNet). In this model, we process the unimodal aspect-aware features via the feature smoothing strategy to partially bridge modality gap. Then we fuse the smoothed features deeply using the multi-channel attention mechanism, to obtain aspect-level sentiment representation with comprehensive representing capability, thereby improving the performance of sentiment classification. Experiments on two benchmark datasets, Twitter2015 and Twitter2017, demonstrate that our model outperforms the second-best model by 1.96% and 0.19% in terms of Macro-F1, respectively. Additionally, ablation studies provide evidence supporting the efficacy of each of our proposed modules. We release the code at:
https://github.com/YunjiaCai/MSFNet
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ISSN: | 2296-424X 2296-424X |
DOI: | 10.3389/fphy.2023.1187503 |