Xiaolin Du

Short Text Classification: A Survey

With the recent explosive growth of e-commerce and online communication, a new genre of text, short text, has been extensively applied in many areas. So many researches focus on short text mining. It is a challenge to classify the short text owing to its natural characters, such as sparseness, large-scale, immediacy, non-standardization. It is difficult for traditional methods to deal with short text classification mainly because too limited words in short text cannot represent the feature space and the relationship between words and documents. Several researches and reviews on text classification are shown in recent times. However, only a few of researches focus on short text classification. This paper discusses the characters of short text and the difficulty of short text classification. Then we introduce the existing popular works on short text classifiers and models, including short text classification using sematic analysis, semi-supervised short text classification, ensemble short text classification, and real-time classification. The evaluations of short text classification are analyzed in our paper. Finally we summarize the existing classification technology and prospect for development trend of short text classification.

OpinionRings: Inferring and visualizing the opinion tendency of socially connected users

Actors (e.g., people, organizations and nations) of online social networks often express different opinions toward opinion targets (e.g., products, events and political figures). Extracting and visualizing the distributions of different opinions among actors facilitate policy-makers (e.g., business managers and government officials) to develop informed decisions promptly. In this paper, by extending the notion of signed networks, we first provide a formal definition of opinion networks which are networks of actors who hold potentially different opinions against specific targets. Another main contribution of our research is the development of a visualization method called OpinionRings to infer and visualize the actual and the potential opinions of different groups of actors. In particular, the proposed OpinionRings method leverages three concentric rings with various colors and widths to highlight different groups of actors and their opinions. One unique feature of the OpinionRings method is that the inclination of an actor, who originally holds a neutral opinion polarity, to adopt a positive or negative opinion polarity can be estimated according to the color of the actor and the distance to other actors with known opinion polarities. A series of objective quantitative experiments and subjective user-based evaluation show that the proposed OpinionRings method significantly outperforms the traditional visualization methods in terms of cohesiveness of displays, informativeness of visualized contents, and inference power of the visualization scheme. The practical implication of our research is that business managers or government officials can apply our proposed computational method to extract and visualize valuable social intelligence from online social networks to facilitate their decision-making processes.

Multi-view learning via multiple graph regularized generative model

Topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), have shown impressive success in many fields. Recently, multi-view learning via probabilistic latent semantic analysis (MVPLSA), is also designed for multi-view topic modeling. These approaches are instances of generative model, whereas they all ignore the manifold structure of data distribution, which is generally useful for preserving the nonlinear information. In this paper, we propose a novel multiple graph regularized generative model to exploit the manifold structure in multiple views. Specifically, we construct a nearest neighbor graph for each view to encode its corresponding manifold information. A multiple graph ensemble regularization framework is proposed to learn the optimal intrinsic manifold. Then, the manifold regularization term is incorporated into a multi-view topic model, resulting in a unified objective function. The solutions are derived based on the Expectation Maximization optimization framework. Experimental results on real-world multi-view data sets demonstrate the effectiveness of our approach.

Multi-opinion Ring: visualizing and predicting multiple opinion orientations in online social media

In the era of the Social Web, actors (e.g. people, organizations, nations, etc) of online social media often voice out their opinions towards a variety of opinion targets. Extracting and visualizing distributions of multiple opinions among actors facilitates individuals or organizations to extract valuable social intelligence from online social media. The main contribution of our research reported in this paper is the development of a novel opinion analysis methodology named Multi-opinion Ring for visualizing and predicting multiple opinion orientations held by different groups of actors in online social media. In particular, the proposed Multi-opinion Ring method combines visualization techniques with machine learning methods to predict the opinion inclinations of actors who are originally neutral to different opinion targets. A series of controlled experiments, user-based evaluations, and case studies show that the proposed Multi-opinion Ring method significantly outperforms classical visualization methods in terms of the cohesiveness of the graphical layout and the informativeness of the visualized contents.

A generative model with hypergraph regularizers for protein function prediction

Heterogeneous data sources and multi-label are two important characteristics of protein function prediction. They describe protein data from two different aspects. However, it is of considerable challenge to integrate multiple data sources and multi-label simultaneously for predicting protein functions, especially when there are only a limited number of labeled proteins. In this paper, we propose a generative model with hypergraph regularizers algorithm, called GMHR, for predicting proteins with multiple functions. The GMHR algorithm integrates all data sources that are available, including protein attribute features, interaction networks, label correlations, and unlabeled data. Experimental results on the real-world datasets predicting the functions of proteins demonstrate the superiority of our proposed method compared with the state-of-the-art baselines.

An area-adaptive multi-level layout for social network visualisation

We describe an area-adaptive multi-level layout for visualising social networks. This area-adaptive layout can visualise social networks according to the display area and community structure, which could reasonably utilise the display area and enhance the community features. The whole process consists of two parts: graph multi-layered compression and top-down multi-level layout. The multi-layered compression process groups vertices to form clusters and then abstracts the clusters as new vertices to define a new graph and is repeated until the graph size falls below some threshold. Based on the compressed graph, we optimise the display area to top-down positions all vertices. We have evaluated our layout on several well-known data sets. The experimental results show that our layout outperforms the state-of-the-art methods.

SGP: Sampling Big Social Network Based on Graph Partition

Deriving a representative sample from a big social network is essential for many Internet services that rely on accurate analysis of big social data. A good sampling method for social network should be able to generate small sample networks with similar structures as original big network. In this paper, we propose SGP, a new big social network sampling algorithm based on graph partition. In SGP, original network is firstly partitioned into several sub-networks that will be sampled evenly. This procedure enables SGP to effectively maintain the topological similarity and community structure similarity between the sampled network and its original network. We have evaluated SGP on several well-known data sets. The experimental results show that SGP outperforms six state-of-the-art methods.