Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog

2015-2016 > Academic research: refereed > Publication in refereed journa

A Net Loan Monitoring Platform for University Students Based on Visual Micro-blog

In order to prevent college students from tragic events, which mostly result from falling into illegal net loan, a net loan risk monitoring platform of college students was established based on the microblog visualization. Based on the calculation model of risk degree (CMRD) and the calculation model of relational closeness (CMRC), a visual system, a user relationship analysis system and an early warning system were constructed on the platform. Through the CMRD, the risk degree of micro-blog net loan contents can be worked out to decide whether warning behaviors are necessary. Through the CMRC, the closeness of net loan bloggers with users can be obtained a propagation map. The key nodes can be cut off by internet security officers in time, which can prevent further propagation of the contents of the microblog net loan. The platform can effectively alleviate the tense situation of college students and illegal online loans

Temporal Exploration in 2D Visualization of Emotions on Twitter Stream

As people freely express their opinions toward a product on Twitter streams without being bound by time, visualizing time pattern of customers emotional behavior can play a crucial role in decision-making. We analyze how emotions are fluctuated in pattern and demonstrate how we can explore it into useful visualizations with an appropriate framework. We manually customized the current framework in order to improve a state-of-the-art of crawling and visualizing Twitter data. The data, post or update on status on the Twitter website about iPhone, was collected from U.S.A, Japan, Indonesia, and Taiwan by using geographical bounding-box and visualized it into two-dimensional heat map, interactive stream graph, and context focus via brushing visualization. The results show that our proposed system can explore uniqueness of temporal pattern of customers emotional behavior

Leveraging writing systems changes for deep learning based Chinese affective analysis

Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an alphabet-based writing system. This phenomenon is referred to as writing systems changes (WSCs). Past studies have shown that WSCs often reflect unfiltered immediate affections. However, the use of WSCs poses more challenges in Natural Language Processing tasks because WSCs can break the syntax of the major text. In this work, we present our work to use WSCs as an effective feature in a hybrid deep learning model with attention network. The WSCs scripts are first identified by their encoding range. Then, the document representation of the text is learned through a Long Short-Term Memory model and the minor text is learned by a separate Convolution Neural Network model. To further highlight the WSCs components, an attention mechanism is adopted to re-weight the feature vector before the classification layer. Experiments show that the proposed hybrid deep learning method which better incorporates WSCs features can further improve performance compared to the state-of-the-art classification models. The experimental result indicates that WSCs can serve as effective information in affective analysis of the social media text

Extracting Geospatial Information from Social Media Data for Hazard Mitigation, Typhoon Hato as Case Study (Short Paper)

With social media widely used for interpersonal communication, it has served as one important channel for information creation and propagation especially during hazard events. Users of social media in hazard-affected area can capture and upload hazard information more timely by portable and internet-connected electric devices such as smart phones or tablet computers equipped with (Global Positioning System) GPS devices and cameras. The information from social media(e.g. Twitter, facebook, sina-weibo, WebChat, etc.) contains a lot of hazard related information including texts, pictures, and videos. Most important thing is that a fair proportion of these crowd-sourcing information is valuable for the geospatial analysis in Geographic information system (GIS) during the hazard mitigation process. The geospatial information (position of observer, hazard-affected region, status of damages, etc) can be acquired and extracted from social media data. And hazard related information could also be used as the GIS attributes. But social media data obtained from crowd-sourcing is quite complex and fragmented on format or semantics. In this paper, we introduced the method how to acquire and extract fine-grained hazard damage geospatial information. According to the need of hazard relief, we classified the extracted information into eleven hazard loss categories and we also analyzed the public\u27s sentiment to the hazard. The 2017 typhoon "Hato" was selected as the case study to test the method introduced