Evaluation of Weighted Graph and Neural Network Models on Predicting the Valence-Arousal Ratings of Chinese Words

Abstract

Automatic sentiment information extraction of social network articles has many essential applications. Following the valence-arousal space framework, in this paper, two approaches including (1) a weighted graph (WG) and (2) a neural network (NN) model that could predict the valence-arousal ratings of words are evaluated on the Chinese valence-arousal words (CVAW) database provide by the IALP-2016 shared task. According the official evaluation results, our NN systems achieved (0.621,1.165) MAEs and (0.853,0.631) PCCs for valence and arousal predictions. Compared with the results of other participants, the performance of our systems are quite nice.