[1]罗正军,柯铭菘,周德群.基于改进型 LSTM 的文本情感分析模型研究[J].计算机技术与发展,2020,30(12):40-44.[doi:10. 3969 / j. issn. 1673-629X. 2020. 12. 007]
　LUO Zheng-jun,KE Ming-song,ZHOU De-qun.Research on Text Sentiment Analysis Model Based on Improved LSTM[J].,2020,30(12):40-44.[doi:10. 3969 / j. issn. 1673-629X. 2020. 12. 007]
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基于改进型 LSTM 的文本情感分析模型研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年12期
页码:
40-44
栏目:
智能、算法、系统工程
出版日期:
2020-12-10

文章信息/Info

Title:
Research on Text Sentiment Analysis Model Based on Improved LSTM
文章编号:
1673-629X(2020)12-0040-05
作者:
罗正军; 柯铭菘; 周德群
南京航空航天大学,江苏 南京 210016
Author(s):
LUO Zheng-jun; KE Ming-song; ZHOU De-qun
Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
关键词:
文本情感分析; 机器学习; 长短期记忆模型; 梯度下降; 损失函数
Keywords:
text sentiment analysis; machine learning; long and short term memory model; gradient descent; loss function
分类号:
TP315
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 12. 007
摘要:
文本情感分析是自然语言处理领域的一大研究方向。文本情感分析本质上属于文本二分类问题,问题的核心是将一段文本所表达的情感分为正向和负向两类。传统的文本分类算法在进行文本情感分析时,不能很好地考虑到词与词之间的关联性以及词语之间的极性转移。针对 LSTM 神经网络模型在文本情感分析中的不足,设计并提出了基于改进型LSTM 的文本情感分析模型。 为了降低在原始 LSTM 模型中采用随机梯度下降法进行参数更新所带来的不确定性,提出一种基于向量空间的伪梯度下降法。 在迭代过程中,为了减轻模型准确率的振荡现象,提出带有修正项的二元交叉熵损失函数,使改进后的模型有选择性地针对分类模糊的数据进行更新。 实验结果表明,改进后的模型在分类正确率以及迭代效率上有所改进。
Abstract:
Text sentiment analysis is a major research direction in the field of natural language processing. Text sentiment analysis is essentially a text binary classification problem. The core of the problem is    to divide the sentiment expressed by a text into two categories:positive and negative. The traditional text classification algorithm cannot well take into account the association between words and the polarity transfer between words when performing text sentiment analysis. Aiming at the shortcomings of LSTM neural network model in text sentiment analysis,we design and propose a text sentiment analysis model based on improved LSTM. In order to reduce the uncertainty caused by the stochastic gradient descent method for parameter updating in the original LSTM model,a pseudo gradient descent method based on vector space is proposed.During the iterative process,in order to reduce the oscillation of the accuracy of the model,a binary cross-entropy loss function with a correction term is proposed, so that the improved model could be selectively updated for the fuzzy data. The experiment shows that the improved model has improved classification accuracy and iteration efficiency.

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更新日期/Last Update: 2020-12-10