Analyzing the sentiments of people from social media content through text, speech, and images is becoming vital in a variety of applications. Many existing research studies on sentiment analysis rely on textual data, and similar to the sharing of text, users of social media share more photographs and videos. Compared to text, images are said to exhibit the sentiments in a much better way. So, there is an urge to build a sentiment analysis model based on images from social media. In our work, we employed different transfer learning models, including the VGG-19, ResNet50V2, and DenseNet-121 models, to perform sentiment analysis based on images. They were fine-tuned by freezing and unfreezing some of the layers, and their performance was boosted by applying regularization techniques. We used the Twitter-based images available in the Crowdflower dataset, which contains URLs of images with their sentiment polarities. Our work also presents a comparative analysis of these pre-trained models in the prediction of image sentiments on our dataset. The accuracies of our fine-tuned transfer learning models involving VGG-19, ResNet50V2, and DenseNet-121 are 0.73, 0.75, and 0.89, respectively. When compared to previous attempts at visual sentiment analysis, which used a variety of machine and deep learning techniques, our model had an improved accuracy by about 5% to 10%. According to the findings, the fine-tuned DenseNet-121 model outperformed the VGG-19 and ResNet50V2 models in image sentiment prediction.

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Visual Sentiment Analysis Using Deep Learning Models with Social Media Data

Efficiency of convolutional neural networks (CNN) based image classification for monitoring construction related activities: A case study on aggregate mining for concrete production

Despite various economic crisis situations around the world, the courier and delivery service market continues to be revitalized. The parcel shipping volume in Korea is currently 3.37 billion parcels, achieving a growth rate of about 140% compared to 2012, and 70% of parcels are from metropolitan areas. Given the above statistics, this paper focused on the development of an underground logistics system (ULS), in order to conduct a study to handle the freight volume in a more eco-friendly manner in the center of metropolitan areas. In this paper we first analyzed the points at which parcel boxes were damaged, based on a ULS. After collecting image data of the parcel boxes, the damaged parcel boxes were detected and classified using computerized methods, in particular, a convolutional neural network (CNN), MobileNet. For image classification, Google Colaboratory notebook was used and 4882 images were collected for the experiment. Based on the collected dataset, when conducting the experiment, the accuracy, recall, and specificity of classification for the testing set were 84.6%, 82% and 88.54%, respectively,. To validate the usefulness of the MobileNet algorithm, additional experiments were performed under the same conditions using other algorithms, VGG16 and ResNet50. The results show that MobileNet is superior to other image classification models when comparing test time. Thus, in the future, MobileNet has the potential to be used for identifying damaged boxes, and could be used to ensure the reliability and safety of parcel boxes based on a ULS.

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Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet

Performance evaluation of short-term cross-building energy predictions using deep transfer learning strategies

An embedded solution for fault detection and diagnosis of photovoltaic modules using thermographic images and deep convolutional neural networks

Transfer learning is used to reuse the pre-trained model. Transfer learning uses the knowledge which was gained from the previous task. Transfer learning is most generally used in image classification, image prediction and natural language processing. Some of the example for the natural language processing it includes sentiment analysis, text auto complete etc. The literature shows that deep learning performance is relatively more when compared with machine learning technique for the large data set. In this paper pre trained models such as MobileNet, MobileNetV2, VGG16, VGG19 and ResNet50 has been used for image classification and prediction. For the image classification and prediction Google Colab notebook has been used. The performance of the system depends on the GPU system hence results are tested in Google colab notebook. The result shows that MobileNetV2 performance is relatively better than other pre trained models. MobileNetV2 uses the less number of parameters as compared with other trained model. ResNet50 accuracy is more when it compared with other trained model with the ImageNet dataset. In the future enhancement transfer learning may be used for natural language processing to obtain highest accuracy.

Niranjan N. Chiplunkar

Papers

Image classification and prediction using transfer learning in colab notebook