Sentiment classification on customer feedback data

It is demonstrated that it is possible to perform automatic sentiment classification in the very noisy domain of customer feedback data by using large feature vectors in combination with feature reduction and the addition of deep linguistic analysis features to a set of surface level word n-gram features contributes consistently to classification accuracy.

Abstract

We demonstrate that it is possible to perform automatic sentiment classification in the very noisy domain of customer feedback data. We show that by using large feature vectors in combination with feature reduction, we can train linear support vector machines that achieve high classification accuracy on data that present classification challenges even for a human annotator. We also show that, surprisingly, the addition of deep linguistic analysis features to a set of surface level word n-gram features contributes consistently to classification accuracy in this domain.