Recognition of Multiple Emotion States of Words using Multi-engine Voting System

Abstract

In this paper, we propose a multi-engine voting system to recognize the multiple emotion states expressed in the Chinese words according to the word identity and the context information, which can better reflect the people's real emotion states compared to previous works focused on single emotion analysis. Conditional Random Field and Maximum Entropy are employed to learn two probabilistic models to predict the possible emotions and assign the corresponding probability scores to the eight basic emotion types as well as the no-emotion type. To solve the problem of imbalanced data source, we introduce a threshold to improve the recognition of no-emotion and other word emotions. Our experiment achieves promising results which prove that multiple emotion states can be better recognized by the multi-engine system rather than either Conditional Random Field or Maximum Entropy.