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Social media contains a plethora of information in the form of text, images, videos, and other data. Users across the globe are increasingly sharing their data on various social media platforms. Sentiment analysis of data, such as text, images, and videos are widely used to understand the feelings of users. In recent years, the convolutional neural network (CNN) has been extensively applied for various applications. The cloud computing environment is a popular service due to its reliability, availability, and easy software integration. However, CNN models are deep neural networks that have a high computational cost. There is a need for CNN models which utilize lesser computational resources especially when these models are deployed in a cloud environment due to the remote physicality of servers, resource optimization, and infrastructure cost reduction. In this research, Gabor filters are integrated with CNN models to improve image sentiment analysis in a cloud environment, with advantages such as the reduction in computation energy and time, the elimination of the need for pre-trained models, and a perceived accuracy improvement. Two variants of Gabor-CNN (G-CNN) models with a different number of pooling and normalization layers are developed. The proposed G-CNN is trained and tested using  five  standard databases as SentiBank, Twitter, MVSO, MultiView&#x005F;I, and MultiView&#x005F;II. Maximum classification accuracies of 91.71%, 92.52%, 97.39%, 90.88%, and 91.31% are obtained on SentiBank, Twitter, MVSO, MultiView&#x005F;I, and MultiView&#x005F;II databases respectively using the developed models. The proposed G-CNN model has provided an accuracy of 92.76% on average.

Gabor CNN based Intelligent System for Visual Sentiment Analysis of Social Media Data on Cloud Environment