基于深度神经网络的微博文本情感倾向性分析

随着新型社交媒体的发展，作为传播网络舆论的重要媒介，微博已然成为挖掘民意的平台.自然语言处理技术可以从微博文本中提取有效情感信息，为网络舆情监控、预测潜在问题及产品分析等提供科学的决策依据.为了克服现有的浅层学习算法对复杂函数表示能力有限的问题，本文尝试融合深度学习的思想，提出基于Word2Vec和针对长短时记忆网络改进的循环神经网络的方法进行中文微博情感分析.在两万多条中文标注语料上进行训练实验，实验数据与SVM、RNN、CNN作对比，对比结果证明，本文提出的情感分析模型准确率达到了91.96%，可以有效提高微博文本情感分类的正确率.

Chinese Weibo Sentiment Analysis Based on Deep Neural Network

With the development of new social media, Weibo, as an important media for the dissemination of public opinion, has become a platform for the excavation of public opinion. Natural Language Processing technology can extract effective emotional information from Weibo texts, and provide scientific decision-making basis for monitoring network public opinion, forecasting potential problems, and product analysis. In order to overcome the limitation of the existing shallow learning algorithm for complex function expression, this study attempts to integrate the idea of deep learning, and puts forward an improved recurrent neural network based on Word2Vec and long-term memory network to analyze Chinese Weibo emotion. In the more than 20 000 Chinese corpus of training experiment, the experimental data with SVM, RNN, and CNN are compared, comparison results show that the emotion analysis model proposed in this study reaches the accuracy rate of 91.96%, thus it can effectively improve the accuracy of the Weibo text sentiment classification.