Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-channel Attentive Graph Convolutional Network With Sentiment
  Fusion For Multimodal Sentiment Analysis   Nowadays, with the explosive growth of multimodal reviews on social media
platforms, multimodal sentiment analysis has recently gained popularity because
of its high relevance to these social media posts. Although most previous
studies design various fusion frameworks for learning an interactive
representation of multiple modalities, they fail to incorporate sentimental
knowledge into inter-modality learning. This paper proposes a Multi-channel
Attentive Graph Convolutional Network (MAGCN), consisting of two main
components: cross-modality interactive learning and sentimental feature fusion.
For cross-modality interactive learning, we exploit the self-attention
mechanism combined with densely connected graph convolutional networks to learn
inter-modality dynamics. For sentimental feature fusion, we utilize multi-head
self-attention to merge sentimental knowledge into inter-modality feature
representations. Extensive experiments are conducted on three widely-used
datasets. The experimental results demonstrate that the proposed model achieves
competitive performance on accuracy and F1 scores compared to several
state-of-the-art approaches.

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The categories of this paper are: stat
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg