FPN_GCT: Feature Pyramid Network Gated Channel Transformation for Automatic Image Sentiment Analysis

Abstract

The field of aspect-level sentiment classification has gained significant attention in recent years, but limited image datasets have hindered the effectiveness of neural network models. To address the issue, we present a novel approach, Feature Pyramid Network Gated Channel Transformation (FPN_GCT) that can automatically extract highly fine-grained sentiment information from images. Our approach utilizes Gated Channel Transformation (GCT) for accurate image sentiment analysis and incorporates ResNet and Reduced Layer to enhance the model's novelty. We validate the performance of our model on the Twitter and CrowdFlower sentiment analysis datasets and demonstrate that it outperforms existing techniques including VGG-19, DenseNet121, ResNet50V2, SVM, MemNet, and RAM, with an accuracy of 91.21 for the CrowdFlower dataset and 74.47 for the Twitter dataset. Our approach offers a substantial advancement in the field of image sentiment analysis and has the potential to enhance the accuracy of aspect-level sentiment classification tasks, paving the way for more accurate and efficient sentiment analysis in various applications.