基于深度表示学习的跨领域情感分析

Sentiment Analysis in Cross-Domain Environment with Deep Representative Learning

【目的】通过在标注资源丰富的源领域中学习,并将目标领域的文档投影到与源领域相同的特征空间中去,从而解决目标领域因数据量较小难以获得好的分类模型的问题。【方法】选择亚马逊在线购物网站在书籍、DVD和音乐类目下的中文、英文和日文评论作为实验数据,在卷积神经网络和结构对应学习的基础上提出跨领域深度表示模型(CDDRM),以实现不同领域环境下的知识迁移,并将其应用到跨领域情感分析任务之中。【结果】实验结果表明,CDDRM在跨领域环境下最优的F值达到0.7368,证明了该模型的有效性。【局限】CDDRM针对长文本的跨领域情感分类F值仍然有待提升。【结论】知识迁移能够解决监督学习在小数据集上难以获得好的分类效果的问题,与传统监督学习的基本假设相比,它并不要求训练集和测试集服从相同或相似的数据分布。

[Objective] The study trains the model with the source domain of rich labeling/tagging data and to project the source and target domain documents into the same feature space. This paper tries to solve the performance issue facing the target domain due to the lack of data. [Methods] First, we collected the Chinese, English and Japanese comments on books, DVDs and music from Amazon. Then, we proposed a Cross Domain Deep Representation Model （CDDRM） based on the Convolutional Neural Network （CNN） and Structural Correspondence Learning （SCL） techniques. Finally, we conducted cross-domain knowledge transfer and sentiment analysis. [Results] We found the best F value of CDDRM was 0.7368, which indicated the effectiveness of the proposed model. [Limitations] The F1 value of our model on long articles needs to be improved. [Conclusions] Transfer learning could help supervised learning obtain good classification results with small training sets. Compared with traditional methods, CDDRM does not require the training and testing sets having same or similar data structure.