Word Vector Modeling for Sentiment Analysis of Product Reviews

Yuan Wang, Zhaohui Li, Jie Liu, Zhicheng He, Yalou Huang, Dong Li

Abstract: Recent years, an amount of product reviews on the internet have become an important source of information for potential customers. These reviews do help to research products or services before making purchase decisions. Thus, sentiment analysis of product reviews has become a hot issue in the field of natural language processing and text mining. Considering good performances of unsupervised neural network language models in a wide range of natural language processing tasks, a semi-supervised deep learning model has been proposed for sentiment analysis. The model introduces supervised sentiment labels into traditional neural network language models. It enhances expression ability of sentiment information as well as semantic information in word vectors. Experiments on NLPCC2014 product review datasets demonstrate that our method outperforms the traditional methods and methods of other teams.