Sentiment classification is a key task in sentiment analysis, reviews mining, and other text mining applications. Various models have been proposed to build sentiment classifiers, but the classification performances of some existing methods are not good enough. Meanwhile, as a subproblem of sentiment classification, positive and unlabeled learning (PU learning) problem widely exists in real-world cases, but it has not been given enough attention. In this article, we aim to solve the two problems in one framework. We first build a model for traditional sentiment classification based on adversarial learning, attention mechanism, and long short-term memory (LSTM) network. We further propose an enhanced adversarial learning method to tackle PU learning problem. We conducted extensive experiments in three real-world datasets. The experimental results demonstrate that our models outperform the compared methods in both traditional sentiment classification problem and PU learning problem. Furthermore, we study the effect of our models on word embedding. Finally, we report and discuss the sensitivity of our models to parameters.

中文翻译：

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对抗性学习和注意机制的情感分类

情感分类是情感分析，评论挖掘和其他文本挖掘应用程序中的关键任务。已经提出了用于建立情感分类器的各种模型，但是一些现有方法的分类性能还不够好。同时，作为情感分类的一个子问题，积极和无标签学习（PU学习）问题在现实世界中广泛存在，但没有得到足够的重视。在本文中，我们旨在解决一个框架中的两个问题。我们首先基于对抗性学习，注意力机制和长短期记忆（LSTM）网络建立传统情感分类的模型。我们进一步提出了一种增强的对抗学习方法来解决PU学习问题。我们在三个现实世界的数据集中进行了广泛的实验。实验结果表明，在传统情感分类问题和PU学习问题上，我们的模型均优于比较方法。此外，我们研究了模型对词嵌入的影响。最后，我们报告并讨论模型对参数的敏感性。