News video story sentiment classification and ranking

In this paper, we present a novel approach for news video story sentiment analysis. Two research challenges are addressed: news video story sentiment classification and ranking. For classification, a graph based semi-supervised learning approach is utilized to classify the news stories into sentiment classes. Graph based semi-supervised learning is able to tackle the problem of lacking labeled data. After classification, two sentiment classes are obtained: positive and negative. In order to project the news videos into sentiment space, a multimodal approach by fusing the text sentiment and visual representation scores is adopted to rank the videos in each class. For sentiment representation, inter and intra sentiment class analysis is conducted based on affinity propagation clustering and PageRank algorithm. A user study is conducted to evaluate the video ranking performance. The experimental results on the selected topics are promising and demonstrate the proposed approach is effective.