摘要
随着自然语言处理科学的迅猛发展,情感分析作为其重要的一个分支广泛应用于社交网络平台上,尤其是微博由于其传播广泛且蕴涵丰富的情感信息而备受学者青睐。为解析微博中表达的情感信息以及深入挖掘其蕴涵的潜在感情,在降噪自动编码器的深度模型之上研究探索改进了这个深度学习模型。降噪自动编码器的工作特点是在引入噪声的干扰之下实现对原始输入的还原,而其改进模型的优势在于考虑到了噪声的多样性和复杂性,并通过深度学习训练加强模型的原始特征复原能力,以此来克服不可预判的原始输入噪声。通过分别使用SVM、降噪自动编码器模型以及改进的模型进行情感分析实验,对比分类效果而得出改进的深度模型对微博文字情感把握更准确而且抗干扰能力及鲁棒性有所提升的结论。
With the rapidly development of natural language processing science, sentiment analysis as one of its important branches are widely used in social network platform. Especially the micro-blogging which are wide dissemination and contains a wealth of information on the emotional and highly favored by scholars. Due to the micro-blogging widely spread and contains rich human emotional information, it quickly become the research object of the Chinese and foreign scholars. To analysis the ex- pression of human emotion in the micro-blogging and even digging its inherent sentiment, this paper made a further on the de- noising auto-encoder（DAE） model and explored a new method to improve it. The characteristics of DAE was to achieve the re- duction of the original input under the interference of noise,while the advantage of the new model were take into account the diversity and complexity of noise, and to strengthen the resilience of the original features of the model through the deep learning training,then the result was overcame the original input noise which could not prediction. At last, by separately using SVM, DAE model and the improved model made sentiment analysis experiment, comparing the classification results indicate that the improved model（IDAE） are more accurate in micro-blogging sentiment analysis. Moreover,its anti-interference ability and ro- bustness has improved.
出处
《计算机应用研究》
CSCD
北大核心
2017年第2期373-377,共5页
Application Research of Computers
关键词
降噪自动编码器
微博
情感分析
深度学习
denoising auto-encoder
micro-blogging
sentiment analysis
deep learning
