Sentiment classification aims to detect polarity from a piece of text. The polarity is usually positive or negative, and the text genre is usually product review. The challenges of sentiment classification are that it is hard to capture semantic of reviews, and the labeled data is hard to annotate. Therefore, we propose neural co-training to learn the semantic representation of each review using the neural network model, and learn the information from unlabeled data using a co-training framework. In particular, we use the attention-based bi-directional Gated Recurrent Unit (Att-BiGRU) to model the semantic content of each review and regard different categories of the target product as different views. We then use a co-training framework to learn and predict the unlabeled reviews with different views. Experiment results with the Yelp dataset demonstrate the effectiveness of our approach.

中文翻译：

---

使用产品属性进行情感分类的神经协同训练

情感分类旨在检测一段文本的极性。极性通常是正面或负面的，文本类型通常是产品评论。情感分类的挑战在于难以捕捉评论的语义，标记数据难以注释。因此，我们建议神经协同训练使用神经网络模型学习每条评论的语义表示，并使用协同训练框架从未标记的数据中学习信息。特别是，我们使用基于注意力的双向门控循环单元（Att-BiGRU）对每条评论的语义内容进行建模，并将目标产品的不同类别视为不同的视图。然后，我们使用协同训练框架来学习和预测具有不同观点的未标记评论。Yelp 数据集的实验结果证明了我们方法的有效性。