基于卷积神经网络参数优化的中文情感分析

摘要

The training of Convolutional Neural Network(CNN)model requires designers to set a large number of model parameters.Because the sensitivity of the model to various parameters is different,the experimental results are poor.To address the problem,this paper provides an analysis on Chinese text sentiment analysis and designs a one layer CNN with influence factors of different models,including the dimensionality of word vectors,the training scale of word vectors,slide window size,regularization method,and so on.Chinese sentiment classification experiment is conducted under different influence factors.According to the results,the sensitivity degree of the CNN on various parameters when dealing with Chinese sentiment analysis and the optimization of specific model parameters are proposed.%卷积神经网络模型的训练需要设计者指定大量模型参数,但因模型对各类参数的敏感度不一,导致实验效果不佳.针对上述问题,研究中文文本情感分析,以词向量维度、词向量训练规模、滑动窗口大小和正则化方法等作为不同模型的影响因素,设计单层卷积神经网络,在不同影响因素下分别进行中文情感分类实验,并根据结果得出卷积神经网络在处理中文情感分析时对各类参数的敏感程度和具体的模型参数优化建议.