基于深度表示学习的跨领域情感分析
发布时间：2018-11-17 21:39
【摘要】：【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。
[Abstract]:[objective] by learning in resource-rich source fields and projecting documents from target domains into the same feature space as source domains, In order to solve the problem that the target area is difficult to obtain good classification model because of the small amount of data. [methods] the Chinese, English and Japanese reviews of Amazon online shopping website under books, DVD and music categories are selected as experimental data. On the basis of convolution neural network and structure correspondence learning, a cross-domain depth representation model (CDDRM),) is proposed to realize knowledge transfer in different domain environments. The experimental results show that the optimal F value of CDDRM in cross-domain environment is 0.7368. It is proved that this model is effective. [limitations] the F value of CDDRM's cross-domain affective classification for long text still needs to be improved. [conclusion] knowledge transfer can solve the problem that supervised learning is difficult to obtain good classification effect on small data sets. Compared with the traditional supervised learning hypothesis, it does not require the training set and the test set to be distributed from the same or similar data.
【作者单位】： 中南财经政法大学信息与安全工程学院;武汉大学信息管理学院;
【基金】：国家自然科学基金面上项目“大数据环境下基于领域知识获取与对齐的观点检索研究”(项目编号:71373286);国家自然科学基金青年项目“突发公共卫生事件社交媒体信息主题演化与影响力建模”(项目编号:71603189) 武汉大学人文社会科学青年学者学术发展计划学术团队项目“人机交互与协作创新”(项目编号:Whu2016020)的研究成果之一
【分类号】：TP391.1
本文编号：2339113
[Abstract]:[objective] by learning in resource-rich source fields and projecting documents from target domains into the same feature space as source domains, In order to solve the problem that the target area is difficult to obtain good classification model because of the small amount of data. [methods] the Chinese, English and Japanese reviews of Amazon online shopping website under books, DVD and music categories are selected as experimental data. On the basis of convolution neural network and structure correspondence learning, a cross-domain depth representation model (CDDRM),) is proposed to realize knowledge transfer in different domain environments. The experimental results show that the optimal F value of CDDRM in cross-domain environment is 0.7368. It is proved that this model is effective. [limitations] the F value of CDDRM's cross-domain affective classification for long text still needs to be improved. [conclusion] knowledge transfer can solve the problem that supervised learning is difficult to obtain good classification effect on small data sets. Compared with the traditional supervised learning hypothesis, it does not require the training set and the test set to be distributed from the same or similar data.
【作者单位】： 中南财经政法大学信息与安全工程学院;武汉大学信息管理学院;
【基金】：国家自然科学基金面上项目“大数据环境下基于领域知识获取与对齐的观点检索研究”(项目编号:71373286);国家自然科学基金青年项目“突发公共卫生事件社交媒体信息主题演化与影响力建模”(项目编号:71603189) 武汉大学人文社会科学青年学者学术发展计划学术团队项目“人机交互与协作创新”(项目编号:Whu2016020)的研究成果之一
【分类号】：TP391.1
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