BiLSTM Model With Attention Mechanism for Sentiment Classification on Chinese Mixed Text Comments

Abstract

Sentiment classification has become a significant research topic in natural language processing. As the most popular research method for sentiment classification, deep learning has been applied to various experimental datasets by many scholars and has achieved good results. Aiming at the problems of poor effect and insufficient accuracy of sentiment classification in the current vertical field, and to solve sentiment classification on Chinese mixed text, including both long text and short text, this work proposes an improved BiLSTM-Attention model that can extract features more effectively. The problem of insufficient dependence on long text is resolved by the Bi-directional Long Short-Term Memory (BiLSTM) model, and important information in the text is obtained by the attention mechanism. This study uses online shopping comment datasets for experiments and applies a multiclassification evaluation index to evaluate the model. Experiments support the proposed approach, the accuracy of sentiment classification on mixed text, long text can achieve to 0.9280, 0.9358 respectively, and its practical effect are more advantageous than other sentiment classification methods in terms of classification performance. The experimental results show that this study makes an important contribution to business development and has good domain extensibility.

Topics & Concepts