Manifold-based Sparse Representation For Opinion Mining

This paper focuses on how to enhance feature representation for opinion mining by exploiting nonlinear feature selection methods based on manifold assumption. It is proposed to exploit both manifold assumption and sparse property as prior knowledge for opinion representation to learn intrinsic structure from data. The proposed algorithm is applied to four various common input features on two benchmark datasets, the Internet Movie Database (IMDB) and the Amazon review dataset. The experiments reveal that the proposed algorithm yields considerable enhancements in terms of F-measure, accuracy, and other standard performance measures compared to the combination of state-of-the-art features with various classifiers.