Abstract
Objectives: To implement a novel and hybrid methodology for finding out the positive features when using convolutional neural networks (CNNs) for visual sentiment analysis. To achieve increased accuracy, precision and recall by using this proposed fusion attention methodology. Methods: This study proposes a modified methodology encompassing spatial attention, channel attention as well as squeeze excitation modules. An enhanced approach on the basis of convolutional neural networks was used here which utilizes convolution operators by combining both spatial and channel-based data. Moreover, we have incorporated three considerations like spatial, channel as well as squeeze and excitation at various levels for attaining optimal results. Findings: The accuracy of the existing approaches was 59.88%, 60.06%, 59.28% and 62.89%, but the proposed fusion attention method showed increased accuracy of 64.15%. Similarly, the F1 score of existing approaches are 0.464804, 0.250164, 0.474129 and 0.2574, but the proposed method revealed increased F1 score of 0.512933. Furthermore, the proposed algorithm showed precision and recall of 0.560896 and 0.472526 which were better when compared with the existing approaches like Res-Target, Resnet50, Alexnet and VGG16. Novelty: The novel feature of this proposed fusion attention algorithm was that it incorporates a hybrid approach in which the image together with convolution passes through channel attention, spatial attention as well as squeeze and excitation so as to attain increased accuracy, but most of the existing approaches have used only channel attention and spatial attention modules. In this proposed method, the algorithm performs convolution in 64-bit, 128-bit and 256-bit respectively together in which the three attentions were interchanged in each convolution, which were not prevalent in the existing approaches. Keywords: Fusion attention algorithm; Sentimental image analysis; Convolutional neural networks; Convolution and pooling; Deep neural network
Highlights
A novel and hybrid methodology was proposed for finding out the positive features when using convolutional neural networks (CNNs) for visual sentiment analysis
This proposed work incorporates a hybrid approach in which the image together with convolution passes through channel attention, spatial attention as well as squeeze and excitation so as to attain more accurate results
Considering the existing VGG-16 networks, it possesses 13 convolutional layers as well as 3 fully connected layers. It includes 13 ReLU layers as well as 4 pooling layers. This proposed work specifies a hybrid approach in which the image together with convolution passes through channel attention, spatial attention as well as squeeze and excitation for attaining higher accuracy
Summary
The existing research works revealed that hybrid methods were largely employed concerning deep learning networks. The preprocessing methods as well as post- processing method were evolving largely, in which the former generates optimal inputs for networks while the latter targets in improving the network output result[1–3]. Such integrated frameworks will permit higherlevel feature extraction using CNN offering better results in terms of accuracy when compared with traditional approaches[4–6]. A novel and hybrid methodology was proposed for finding out the positive features when using CNNs for visual sentiment analysis This proposed work incorporates a hybrid approach in which the image together with convolution passes through channel attention, spatial attention as well as squeeze and excitation so as to attain more accurate results. Squeeze and excitation investigates the output to attain more precise classification
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