Bottom up: Exploring word emotions for Chinese sentence chief sentiment classification

Abstract

In this paper we demonstrate the effectiveness of employing basic sentiment components for analyzing the chief sentiment of Chinese sentence among nine categories of sentiments (including “No emotion”). Compared to traditional lexicon based methods, our research explores emotion intensities of words and phrases in an eight dimensional sentiment space as features. An emotion matrix kernel is designed to evaluate inner product of these sentiment features for SVM classification with O(n) time complexity. Experimental result shows our method significantly improves performance of sentiment classification.