Text Sentiment Analysis Based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure  

作　　者：Zhengfang He Cristina E.Dumdumaya Ivy Kim D.Machica 

机构地区：[1]School of Intelligent Science and Engineering,Yunnan Technology and Business University,Kunming,650000,China [2]College of Information and Computing,University of Southeastern Philippines,Davao City,Davao del Sur,Philippines

出　　处：《Intelligent Automation & Soft Computing》2023年第7期639-654,共16页智能自动化与软计算（英文）

基　　金：supported by Yunnan Provincial Education Department Science Foundation of China under Grant construction of the seventh batch of key engineering research centers in colleges and universities(Grant Project:Yunnan College and University Edge Computing Network Engineering Research Center).

摘　　要：Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,measure,and investigate affective states and subjective data.Sentiment analy-sis algorithms include emotion lexicon,traditional machine learning,and deep learning.In the text sentiment analysis algorithm based on a neural network,multi-layer Bi-directional long short-term memory(LSTM)is widely used,but the parameter amount of this model is too huge.Hence,this paper proposes a Bi-directional LSTM with a trapezoidal structure model.The design of the trapezoidal structure is derived from classic neural networks,such as LeNet-5 and AlexNet.These classic models have trapezoidal-like structures,and these structures have achieved success in the field of deep learning.There are two benefits to using the Bi-directional LSTM with a trapezoidal structure.One is that compared with the single-layer configuration,using the of the multi-layer structure can better extract the high-dimensional features of the text.Another is that using the trapezoidal structure can reduce the model’s parameters.This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2(STS-2)for experiments.It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances.However,the trapezoidal structure model parameters are 35.75%less than the normal structure model.

关 键 词：Text sentiment Bi-directional LSTM Trapezoidal structure 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]