Mining method of public opinion related topic in network multimedia data

Abstract

Social media has become the platform for rumors rapid propagation, more and more users adopt the pictures and videos to express their opinions in order to avoid being detected by text-based approaches, which has greatly affected the efficiency of online public opinion monitoring. For tackling the above-mentioned problem, this paper mainly studies how to extract the related opinions from the multimedia network data. Firstly, three typical events in Sina weibo are selected as the research targets, in which the web crawler is designed to collect the multimedia data. Secondly, the text detection algorithm based on connectionist text proposal network (CTPN) is employed to perform the text localization, and then a fusion method by combining DenseNet and connectionist temporal classification (CTC) is employed to perform text extraction. Finally, an effective algorithm by combining multi granularity-latent Dirichlet allocation (MG-LDA) and jieba is proposed to accurately identify the related topics from the extracted text. The experimental results show that the proposed method can accurately extract the texts from multimedia with different formats, resolutions and colors, and can also extract the texts with different rotating angles. Our research provides the solid foundations for online public opinion monitoring.