Sentiment analysis of online reviews and other user generated content is an important research problem for its wide range of applications.In this paper,we propose a feature-based vector model and a novel weighting alg...Sentiment analysis of online reviews and other user generated content is an important research problem for its wide range of applications.In this paper,we propose a feature-based vector model and a novel weighting algorithm for sentiment analysis of Chinese product reviews.Specifically,an opinionated document is modeled by a set of feature-based vectors and corresponding weights.Different from previous work,our model considers modifying relationships between words and contains rich sentiment strength descriptions which are represented by adverbs of degree and punctuations.Dependency parsing is applied to construct the feature vectors.A novel feature weighting algorithm is proposed for supervised sentiment classification based on rich sentiment strength related information.The experimental results demonstrate the effectiveness of the proposed method compared with a state of the art method using term level weighting algorithms.展开更多
A new finite element method, which is the characteristic-based operator-splitting (CBOS) algorithm, is developed to solve Navier-Stokes (N-S) equations. In each time step, the equations are split into the diffusive pa...A new finite element method, which is the characteristic-based operator-splitting (CBOS) algorithm, is developed to solve Navier-Stokes (N-S) equations. In each time step, the equations are split into the diffusive part and the convective part by adopting the operator-splitting algorithm. For the diffusive part, the temporal discretization is performed by the backward difference method which yields an implicit scheme and the spatial discretization is performed by the standard Galerkin method. The convective part can be discretized using the characteristic Galerkin method and solved explicitly. The driven square flow and backward-facing step flow are conducted to validate the model. It is shown that the numerical results agree well with the standard solutions or existing experimental data, and the present model has high accuracy and good stability. It provides a prospective research method for solving N-S equations.展开更多
基金This work was supported in part by National Natural Science Foundation of China under Grants No.60970052，the Beijing Natural Science Foundation under Grants No.4133084，the Beijing Educational Committee Science and Technology Development Planned under Grants No.KM201410028017 and the Beijing Key Disciplines of Computer Application Technology
文摘Sentiment analysis of online reviews and other user generated content is an important research problem for its wide range of applications.In this paper,we propose a feature-based vector model and a novel weighting algorithm for sentiment analysis of Chinese product reviews.Specifically,an opinionated document is modeled by a set of feature-based vectors and corresponding weights.Different from previous work,our model considers modifying relationships between words and contains rich sentiment strength descriptions which are represented by adverbs of degree and punctuations.Dependency parsing is applied to construct the feature vectors.A novel feature weighting algorithm is proposed for supervised sentiment classification based on rich sentiment strength related information.The experimental results demonstrate the effectiveness of the proposed method compared with a state of the art method using term level weighting algorithms.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41072235, 50809008)the Hong Kong Research Grants Council (Grant No. HKU 7171/06E)+1 种基金the National Basic Research Program of China ("973" Project) (Grant No. 2007CB209400)the Natural Science Foundation of LiaoNing Province of China (Grant No. 20102006)
文摘A new finite element method, which is the characteristic-based operator-splitting (CBOS) algorithm, is developed to solve Navier-Stokes (N-S) equations. In each time step, the equations are split into the diffusive part and the convective part by adopting the operator-splitting algorithm. For the diffusive part, the temporal discretization is performed by the backward difference method which yields an implicit scheme and the spatial discretization is performed by the standard Galerkin method. The convective part can be discretized using the characteristic Galerkin method and solved explicitly. The driven square flow and backward-facing step flow are conducted to validate the model. It is shown that the numerical results agree well with the standard solutions or existing experimental data, and the present model has high accuracy and good stability. It provides a prospective research method for solving N-S equations.
