Sentiment classification via integrating multiple feature presentations

In the bag of words framework, documents are often converted into vectors according to predefined features together with weighting mechanisms. Since each feature presentation has its character, it is difficult to determine which one should be chosen for a specific domain, especially for the users who are not familiar with the domain. This paper explores the integration of various feature presentations to improve the classification accuracy. A general two phases framework is proposed. In the first phase, we train multiple base classifiers with various vector spaces and use these classifiers to predict the class of testing samples respectively. In the second phase, the previous predicted results are integrated into the ultimate class via stacking with SVM. The experimental results demonstrate the effectiveness of our method. Copyright is held by the author/owner(s).