深度学习框架下微博文本情感细粒度研究

情感细粒度分析是情感分析的分支，随着社交网络规模的扩大，简单的划分积极或消极的粗粒度情感分析不能满足实际应用的需要，基于评价对象及其属性的细粒度情感分析得到了重视.近几年深度学习在自然语言处理领域的成功应用给情感细粒度分析提供了新的思路.以NLPCC2013任务二微博数据集为研究对象，探究微博短文本在不同神经网络结构中的情感细粒度分类结果并加入词向量进行优化，最后分析与总结了神经网络微博短文本细粒度情感分析的影响因素及发展方向.

Fine-Grained Analysis and Research of Emotion in Microtext under Framework of Deep Learning

Fine-grained analysis of emotion is a branch of sentiment analysis, with the expansion of social network, the division of simple positive or negative coarse-grained sentiment analysis cannot satisfy the need of practical application. Thus the fine-grained emotional analysis based on evaluation objects and their attributes has received attention in recent years. The successful application of deep learning in the field of natural language processing in recent years provides a new idea for the fine-grained analysis of emotion. Take NLPCC2013 task 2 Weibo data set as the research object, explore the classification results of microtext in different neural network structures and add word vectors for optimization. Finally, the influencing factors and development direction of finer-grained emotion analysis of neural network micro-blog essay are analyzed and summarized.