People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applica...People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user's sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.展开更多
We consider constraint preserving multidimensional evolution equations.A prototypical example is provided by the magnetic induction equation of plasma physics.The constraint of interest is the divergence of the magnet...We consider constraint preserving multidimensional evolution equations.A prototypical example is provided by the magnetic induction equation of plasma physics.The constraint of interest is the divergence of the magnetic field.We design finite volume schemes which approximate these equations in a stable manner and preserve a discrete version of the constraint.The schemes are based on reformulating standard edge centered finite volume fluxes in terms of vertex centered potentials.The potential-based approach provides a general framework for faithful discretizations of constraint transport and we apply it to both divergence preserving as well as curl preserving equations.We present benchmark numerical tests which confirm that our potential-based schemes achieve high resolution,while being constraint preserving.展开更多
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research was supported in part by National Basic Research Program of China （973 Program, No. 2013CB329601, No. 2013CB329604）, National Natural Science Foundation of China （No.91124002, 61372191, 61472433, 61202362, 11301302）, and China Postdoctoral Science Foundation （2013M542560）. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
文摘People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user's sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.
基金E.Tadmor Research was supported in part by NSF grant 07-07949 and ONR grant N00014-091-0385.
文摘We consider constraint preserving multidimensional evolution equations.A prototypical example is provided by the magnetic induction equation of plasma physics.The constraint of interest is the divergence of the magnetic field.We design finite volume schemes which approximate these equations in a stable manner and preserve a discrete version of the constraint.The schemes are based on reformulating standard edge centered finite volume fluxes in terms of vertex centered potentials.The potential-based approach provides a general framework for faithful discretizations of constraint transport and we apply it to both divergence preserving as well as curl preserving equations.We present benchmark numerical tests which confirm that our potential-based schemes achieve high resolution,while being constraint preserving.
