基于深度表示学习和高斯过程迁移学习的情感分析方法

情感分析是自然语言处理领域的重要研究问题。现有方法往往难以克服样本偏置与领域依赖问题,严重制约了情感分析的发展和应用。为此,该文提出了一种基于深度表示学习和高斯过程知识迁移学习的情感分析方法。该方法首先利用深度神经网络获得文本样本的分布式表示,而后基于深度高斯过程,从辅助数据中迁移与测试集数据分布相符的高质量样例扩充训练数据集用于分类器训练,以此提高文本情感分类系统性能。在COAE2014文本情感分类数据集上进行的实验结果显示,该文提出的方法可以有效提高文本情感分类性能,同时可以有效缓解训练数据的样本偏置以及领域依赖问题的影响。

Sentiment Analysis Based on Deep Representation Learning #br# and Gaussian Processes Transfer Learning

Sentiment analysis is an important topic in natural language processing research. Most existing sentiment analysis techniques are difficult to handle the domain dependent and sample bias issues, which restrain the development and application of sentiment analysis. To address these issues, this paper presents a sentiment analysis approach based on deep representation learning and Gaussian Processes transfer learning. Firstly, the distributed representations of text samples are learned based on deep neural network. Next, based on deep Gaussian processes, this approach selects quality samples with the distribution similar to testing dataset from additional dataset to expand the training dataset. The sentiment classifier trained on the expanded dataset is expected to achieve higher performance. The experimental results on COAE2014 dataset show that the proposed approach improved the sentiment classification performance. Meanwhile, this approach alleviates the influences of training sample bias and domain dependence.