文献类型：期刊文献

英文题名：Sentiment analysis of text based on cnn and bi-directional LSTM model

作者：Zhou, Kai Long, Fei

第一作者：Zhou, Kai

通信作者：Long, Fei|[144401c07006f9bfbbbd7]龙飞;[14440e98202afd796d385]龙飞;

机构：[1] College of Big Data and Information Engineering, Guizhou University, Guiyang, China; [2] School of Electrical Engineering, Guizhou Institute of Technology, Guiyang, China

第一机构：College of Big Data and Information Engineering, Guizhou University, Guiyang, China

通信机构：|贵州理工学院电气与信息工程学院;贵州理工学院电气与信息工程学院

年份：2018

外文期刊名：ICAC 2018 - 2018 24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing

收录：EI(收录号：20192907215423)

语种：英文

外文关键词：Automation - Numerical methods - Long short-term memory

摘要：In order to overcome the deficiency of sentiment analysis based on traditional machine learning, which difficulty of effective feature selection and inadequacy of marked training corpus will affect the performance of the classification system, we address the sentiment emotions analysis problem of Chinese product reviews text by combining convolutional neural network (CNN) with bidirectional long-short term memory network (BiLSTM) in this paper. The CNN can extract the sequence features from the global information, and it is able to consider the relationship among these features. The BiLSTM not only solves the long-term dependency problem, but also considers the context of the text at the same time. The result of numerical experiments shows that the proposed model achieves better metrics performance than the state-of-the-art methods. ? 2018 Chinese Automation and Computing Society in the UK - CACSUK.