XRA-Net Framework for Visual Sentiments Analysis
Ashima Yadav, Ayush Agarwal, Dinesh Kumar Vishwakarma · 2019
The exponential growth of social media has motivated people to express themselves in various forms. Visual media is one of the most effective and popular ways of conveying sentiments or opinions on the web as people keeps on uploading millions of photos on famous social networking sites. Hence, Visual Sentiment Analysis is instrumental in monitoring an overview of the broader public consensus behind a specific topic or issue. This work proposes a deep learning-based architecture XRA-Net (Xception Residual Attention based Network) for visual sentiment analysis. Moreover, the performance of the XRA-Net architecture is evaluated on the publicly available real-world Twitter I dataset, which is further composed of three subsets of the dataset: 3-agree, 4-agree, and 5-agree. The accuracy achieved on these datasets are: 79.2%, 81.2%, and 86.4% respectively, which shows that the proposed architecture has outperformed the state-of-the-art results on all the three subsets of Twitter I dataset as it can focus on the most informative features in an input image, which boosts the visual sentiment analysis process.