CKIP Valence-Arousal Predictor for IALP 2016 Shared Task
Hsin-Yang Wang, Wei-Yun Ma · 2016
Sentiment analysis is an important task in natural language processing and computational linguistics. Automatic sentiment analysis has been widely applied to opinion reviews and social media for a variety of applications, such as marketing and customer services. The dimensional approach can provide more fine-grained sentiment analysis in which each vocabulary is assigned two continuous numerical values - valence and arousal. Our goal is to predict the both values for the unseen vocabularies. In this paper we propose a combination of three rating predictors - E-HowNet knowledge based, word embedding based and single character based predictors to predict Chinese vocabularies. In the IALP 2016 Shared Task (Dimensional Sentiment Analysis for Chinese Words), out of 32 teams, our system ranks top1 on the prediction of valence, and ranks top14 on the prediction of arousal.