基于深度学习的短文本情感分析
发布时间：2018-10-09 12:15
【摘要】：互联网尤其是移动互联网的快速发展使其充斥着大量的带有情感的短文本,挖掘这些文本包含的情感信息可以获取大量的商业信息和社会信息。本文从两个方面使用深度学习算法进行文本情感分析研究。第一,有效组合多个深度学习算法,缓解单个算法学习偏置问题。对卷积神经网络稍作修改,作为基础算法,使用boosting和bagging两种算法对多个基础算法进行组合研究,并且采用多个抽样算法提高基础算法的多样性,进而提高整个算法的性能;第二,为文档生成更优的向量表示,减少文档表示中的噪声;短文本特征稀疏,直接使用深度学习算法得到的文档的向量表示包含较多噪声。通过采用多任务学习算法,同时训练多个与情感分类相关的任务,将更多的特征信息回馈到文档的向量表示中,从而减小噪声,提高文档表示的质量。使用多个领域,多种分布的测试集测试相关算法,实验结果表明本文研究算法具有比较强的泛化能力,总体效果基本符合预期。
[Abstract]:With the rapid development of the Internet, especially the mobile Internet, it is flooded with a large number of emotional text books, and the emotional information contained in these texts can be mined to obtain a large amount of business information and social information. In this paper, we use depth learning algorithm to analyze the text emotion from two aspects. First, we can effectively combine multiple depth learning algorithms to alleviate the single algorithm learning bias problem. The convolutional neural network is modified a little, as the basic algorithm, boosting and bagging are used to study the combination of several basic algorithms, and multiple sampling algorithms are used to improve the diversity of the basic algorithm, and then to improve the performance of the whole algorithm. Secondly, a better vector representation is generated for the document to reduce the noise in the document representation, and the vector representation of the document obtained by using the depth learning algorithm directly contains more noise. By using multi-task learning algorithm and training several tasks related to emotion classification, more feature information is returned to the vector representation of documents, thus reducing noise and improving the quality of document representation. The experimental results show that the proposed algorithm has a strong generalization ability and the overall effect is basically in line with the expectation.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.1;TP181
本文编号：2259299
[Abstract]:With the rapid development of the Internet, especially the mobile Internet, it is flooded with a large number of emotional text books, and the emotional information contained in these texts can be mined to obtain a large amount of business information and social information. In this paper, we use depth learning algorithm to analyze the text emotion from two aspects. First, we can effectively combine multiple depth learning algorithms to alleviate the single algorithm learning bias problem. The convolutional neural network is modified a little, as the basic algorithm, boosting and bagging are used to study the combination of several basic algorithms, and multiple sampling algorithms are used to improve the diversity of the basic algorithm, and then to improve the performance of the whole algorithm. Secondly, a better vector representation is generated for the document to reduce the noise in the document representation, and the vector representation of the document obtained by using the depth learning algorithm directly contains more noise. By using multi-task learning algorithm and training several tasks related to emotion classification, more feature information is returned to the vector representation of documents, thus reducing noise and improving the quality of document representation. The experimental results show that the proposed algorithm has a strong generalization ability and the overall effect is basically in line with the expectation.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.1;TP181
【参考文献】
相关会议论文 前1条
1 章剑锋;张奇;吴立德;黄萱菁;;中文评论挖掘中的主观性关系抽取[A];第三届全国信息检索与内容安全学术会议论文集[C];2007年
，本文编号：2259299
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