Learning Semantic Representations for Rating Vietnamese Comments

Abstract

Opinion mining and sentiment analysis has recently become a hot topic in the field of natural language processing and text mining. This paper addresses the problem of overall rating for comments in Vietnamese language. The traditional approach of using bag-of-words for feature representation would cause a very high dimensional feature space and doesn't reflect relationship between words. To capture more linguistic information, this paper provides a new neural network model containing three layers: (1) word embedding; (2) comment representation (i.e. comment feature vector); and (3) comment rating prediction. In which, the word embedding layer is designed to learn word embeddings which can capture semantic and syntactic relations between words, the second layer uses a semantic composition model for comment representation, and the third layer is designed as a perceptron and it stands for predicting overall rating of a comment. In experiment, we use a Vietnamese data set which contains comments on the domain of mobile phone products. Experimental results show that our proposed model outperforms traditional neural network models with comment representations based on bag of word model or word vector averaging.