【摘要】 针对文本情感分类任务中,有情感标注的语料在不同语言中的不均衡问题,结合深度学习和迁移学习,提出一种基于对抗长短时记忆网络(ALSTM)的跨语言文本情感分类方法.设置双语各自独立的特征提取网络和共享特征提取网络,把获取到的特征拼接输入到分类器进行分类.在共享特征提取网络中,设置语言分类器,运用对抗思想优化模型,通过投票法决定文本最终的情感极性.实验表明:该方法可以取得跨语言文本情感分类任务更高的准确度.更多还原

【Abstract】 This paper proposes a cross-lingual sentiment classification method based on adversarial long short term memory(ALSTM)network,which aims at the problem of text sentiment classification in the disparity of emotionally annotated corpus in different languages,combined with deep learning and transfer learning.Bilingual feature extraction networks and a shared feature extraction network are set up,and then the extracted features are merged for classification.In the shared feature extraction network,a language classifier is set up.Using the adversarial idea to optimize the model,and the final polarity of the text depends on the voting results.Experiments show that cross-lingual sentiment classification can achieve higher accuracy by this method.更多还原