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Sentiment Analysis with Improved Adaboost and Transfer Learning Based on Gaussian Process

机译：基于高斯过程的改进Adaboost和迁移学习的情感分析

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摘要

Sentiment analysis is an increasingly important area in NLP to extract opinions and sentiment expressed by humans. Traditional methods are often difficult to tackle the problems of different sample distribution and domain dependence, which seriously limits the development of sentiment classification. In this paper, a novel sentiment analysis method is proposed by combining improved Adaboost and transfer learning based on Gaussian Processes to solve these two problems. A Paragraph Vector Model is employed to obtain the continuous distributed vector representations. Then, Adaboost method is used to choose the most important training features in source training data and auxiliary data. Finally, an asymmetric transfer learning classifier is introduced in Gaussian Processes. It is shown that, compared with the existing algorithms, our method is more effective for the different sample distribution and domain dependence.
机译：情感分析是自然语言处理中提取人类表达的观点和情感的一个日益重要的领域。传统方法通常难以解决样本分布不同和域依赖性不同的问题，这严重限制了情感分类的发展。本文提出了一种新颖的情感分析方法，该方法将改进的Adaboost和基于高斯过程的转移学习相结合来解决这两个问题。采用段落矢量模型来获得连续的分布式矢量表示。然后，使用Adaboost方法从源训练数据和辅助数据中选择最重要的训练特征。最后，在高斯过程中引入了非对称转移学习分类器。结果表明，与现有算法相比，我们的方法对于不同的样本分布和域依赖性更有效。

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