基于Word2Vec文本情感研究与分析
Research and Analysis of Textual Emotion Based on Word2Vec

作者: 尹光花 :中原工学院,计算机学院,河南 郑州;

关键词: 机器学习LIBSVM情感特征Word2Vec情感分类Machine Learning LIBSVM Emotional Characteristics Word2Vec Emotional Classification

摘要:
随着人工智能、机器学习发展,支持向量与传统的神经网络等非线性判定和建模理论相比,因结构简单,理论完备的优点被人们用到文本信息处理领域,解决分类问题。本文针对海量的文本情感数据错综复杂,不能及时准确的掌握文本正负(褒贬极性)信息,提出了深度学习文本情感分类的研究。首先,叙述了文本情感分类的算法思想;然后,引入了文本情感特征和TF-IDF权重计算,通过改进TF-IDF权重,调解优化深度学习Word2Ve词向量 + LIBSVM模型训练互联网文本数据。最后,分类的准确精度达到92.28%。

Abstract: With the development of artificial intelligence, machine learning development, compared with traditional neural network and other non-linear decision and modeling theory, support vector is used in the field of text information processing to solve the classification problem, because of the simple structure and complete theories. This paper puts forward the research on the emotion classification of the depth learning text in view of the intricacies of the massive text emotion data, the inability to grasp the positive and negative information of the text accurately and accurately. Firstly, we introduce the idea of text emotion classification. Then we introduce the text emotion and TF-IDF weight calculation, and improve the TF-IDF weight and mediate the depth of learning Word2Ve word vector + LIBSVM model to train the Internet text data. Finally, the accuracy of the classification reached 92.28%.

文章引用: 尹光花 (2017) 基于Word2Vec文本情感研究与分析。 计算机科学与应用, 7, 944-950. doi: 10.12677/CSA.2017.710107

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