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The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages. [ABSTRACT FROM AUTHOR]

The catenary support components (CSCs) are the most important devices in high-speed railways to support contact lines for powering trains. To estimate the states of CSCs, it is very necessary to locate their positions in a monitoring system based on computer vision. Considering the application scenarios and characteristics of CSCs, an automatic and quick positioning system is designed in this paper to simultaneously position the multiscale CSCs with 12 categories. In the system, an effective framework called CSCs network (CSCNET) is presented, which cascades the coarse positioning network and the fine positioning network to reduce multiscale differences between different CSCs. In the coarse positioning network, a new unsupervised clustering algorithm based on the relative positioning information is proposed to classify the catenary images. Then, a convolutional neural network (CNN) classification network is trained to extract the structural features of catenary images and generate the proposal regions with labels. In the fine positioning network, a modified CNN positioning framework is applied to obtain the accurate positions of CSCs based on the coarse positioning results. Due to the special lightweight structure with a classification network, the relative position information is applied and makes the CSCNET sensitive to small-scale components. The experimental results from some high-speed railway lines in China show that the proposed system has obvious advantages in the CSCs positioning. The mean average precision and frames per second of CSCNET reach 0.837 and 2.17, respectively. Compared with some popular convolutional networks [faster region-based CNN (Faster R-CNN), etc.] and a typical positioning method, the proposed system significantly improves the AP without increasing the computational time. [ABSTRACT FROM AUTHOR]

Sentiment analysis of microblog texts has drawn lots of attention in both the academic and industrial fields. However, most of the current work only focuses on polarity classification. In this paper, we present an opinion mining system for Chinese microblogs called CMiner. Instead of polarity classification, CMiner focuses on more complicated opinion mining tasks - opinion target extraction and opinion summarization. Novel algorithms are developed for the two tasks and integrated into the end-to-end system. CMiner can help to effectively understand the users’ opinion towards different opinion targets in a microblog topic. Specially, we develop an unsupervised label propagation algorithm for opinion target extraction. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. In addition, we build an aspect-based opinion summarization framework for microblog topics. After getting the opinion targets of all the microblog messages in a topic, we cluster the opinion targets into several groups and extract representative targets and summaries for each group. A co-ranking algorithm is proposed to rank both the opinion targets and microblog sentences simultaneously. Experimental results on a benchmark dataset show the effectiveness of our system and the algorithms. [ABSTRACT FROM PUBLISHER]