Enabling 5G: sentimental image dominant graph topic model for cross-modality topic detection

Abstract

Fifth generation mobile networks (5G) is coming into our life and it will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer “smarter” user experience. With the commercial uses of 5G, online social networks and smart phones will spring up again. Then cross-modality data will play a more important role in the daily information dissemination. As an effective way of content analysis, topic detection has attracted much research interest, but conventional topic analysis is undergoing the limitations from the cross-modality heterogenous data. This paper proposes a sentimental image dominant graph topic model, that can detect the topic from the heterogenous data and mine the sentiment of each topic. In details, we design a topic model to transfer both the low-level visual modality and the high-level text modality into a semantic manifold, and improve the discriminative power of CNN feature by jointly optimizing the output of both convolutional layer and fully-connected layer. Furthermore, since the sentimental impact is very significant for understanding the intrinsic meaning of topics, we introduce a semantic score of subjective sentences to calculate the sentiment on the base of the contextual sentence structure. The comparison experiments on the public cross-modality benchmark show the promising performance of our model. So our method using AI technology will facilitate the intellectualization of 5G.