Social media is a major platform for opinion sharing. In order to better understand and exploit opinions on social media, we aim to classify users with opposite opinions on a topic for decision support. Rather than mining text content, we introduce a link-based classification model, named global consistency maximization (GCM) that partitions a social network into two classes of users with opposite opinions. Experiments on a Twitter data set show that: (1) our global approach achieves higher accuracy than two baseline approaches and (2) link-based classifiers are more robust to small training samples if selected properly.

We propose a criminal identity resolution technique that utilizes both personal identity and social identity information. Guided by existing identity theories, we examine three types of identity features, namely personal identity attributes, social behavior attributes, and social relationship attributes. We also explore three matching strategies, namely pair-wise comparison, transitive-closure, and collective resolution. Our experiment on synthetic data sets show that both social behavior and relationship attributes improve the performance of identity matching as compared to the use of personal identity attributes alone. The results also show that the collective relational resolution approach outperformed other approaches in terms of F-measure.

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

With the rapid outstripping of healthcare resources by the demands on hospital care, it is important to find more effective and efficient ways for managing care. This research is aimed at developing new admission prediction models using various pre-hospital variables to help hospital estimate the patients to be admitted. We developed a framework of hospital admission prediction and proposed two novel approaches to capture semantics of chief complaints to enhance prediction. Our experiments on a hospital dataset demonstrated that our proposed models outperformed several benchmark methods.

Abstract User‐generated content on the Web has become an extremely valuable source for mining and analyzing user opinions on any topic. Recent years have seen an increasing body of work investigating methods to recognize favorable and unfavorable sentiments toward specific subjects from online text. However, most of these efforts focus on English and there have been very few studies on sentiment analysis of Chinese content. This paper aims to address the unique challenges posed by Chinese sentiment analysis. We propose a rule‐based approach including two phases: (1) determining each sentence's sentiment based on word dependency, and (2) aggregating sentences to predict the document sentiment. We report the results of an experimental study comparing our approach with three machine learning‐based approaches using two sets of Chinese articles. These results illustrate the effectiveness of our proposed method and its advantages against learning‐based approaches.

Visualizing the intellectual structure of scientific domains using co-cited units such as references or authors has become a routine for domain analysis. In previous studies, paper-reference matrices are usually transformed into reference-reference matrices to obtain co-citation relationships, which are then visualized in different representations, typically as node-link networks, to represent the intellectual structures of scientific domains. Such network visualizations sometimes contain tightly knit components, which make visual analysis of the intellectual structure a challenging task. In this study, we propose a new approach to reveal co-citation relationships. Instead of using a reference-reference matrix, we directly use the original paper-reference matrix as the information source, and transform the paper-reference matrix into an FP-tree and visualize it in a Java-based prototype system. We demonstrate the usefulness of our approach through visual analyses of the intellectual structure of two domains: Information Visualization and Sloan Digital Sky Survey (SDSS). The results show that our visualization not only retains the major information of co-citation relationships, but also reveals more detailed sub-structures of tightly knit clusters than a conventional node-link network visualization.

Identity management is critical for many intelligence and security applications. Identity information is not reliable due to the problems of unintentional errors and intentional deception by the criminals. Most of existing identity matching techniques consider personal identity features only. In this article we propose a PRM-based identity matching technique that takes both personal identity features and social contexts into account. We identify two groups of social context features, namely social activity and social relation features. Experiments show that the social activity features significantly improve the matching performance while the social relation features effectively reduce false positive and false negative.

Abstract Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedi‐cal lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel‐based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel‐based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedi‐cal corpus, we compare different kernel functions for biomedical relation detection and classification. Theexperimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace‐tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.

Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer gene functions. Most previous studies used heuristic approaches based on either local or global information of gene interaction networks to assign unknown gene functions. In this study, we propose a graph kernel-based method that can capture the structure of gene interaction networks to predict gene functions. We conducted an experimental study on a test-bed of P53-related genes. The experimental results demonstrated better performance for our proposed method as compared with baseline methods.

High dimensionality has been a major problem for gene array-based cancer classification. It is critical to identify marker genes for cancer diagnoses. We developed a framework of gene selection methods based on previous studies. This paper focuses on optimal search-based subset selection methods because they evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this paper is the first to introduce tabu search (TS) to gene selection from high-dimensional gene array data. Our comparative study of gene selection methods demonstrated the effectiveness of optimal search-based gene subset selection to identify cancer marker genes. TS was shown to be a promising tool for gene subset selection.

One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference.

Abstract With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online‐message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity‐tracing problem. In this framework, four types of writing‐style features (lexical, syntactic, structural, and content‐specific features) are extracted and inductive learning algorithms are used to build feature‐based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online‐newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple‐language context.