Detecting Opinion Polarities using Kernel Methods

We investigate the application of kernel methods to representing both structural and lexical knowledge for predicting polarity of opinions in consumer product review. We introduce anygram kernels which model lexical information in a significantly faster way than the traditional n-gram features, while capturing all possible orders of n-grams (n) in a sequence without the need to explicitly present a pre-specified set of such orders. We also modify the traditional tree kernel function to compute the similarity based on word embedding vectors instead of exact string match and present experiments using the new models.