The use of user/product information in sentiment analysis is important, especially for cold-start users/products, whose number of reviews are very limited. However, current models do not deal with the cold-start problem which is typical in review websites. In this paper, we present Hybrid Contextualized Sentiment Classifier (HCSC), which contains two modules: (1) a fast word encoder that returns word vectors embedded with short and long range dependency features; and (2) Cold-Start Aware Attention (CSAA), an attention mechanism that considers the existence of cold-start problem when attentively pooling the encoded word vectors. HCSC introduces shared vectors that are constructed from similar users/products, and are used when the original distinct vectors do not have sufficient information (i.e. cold-start). This is decided by a frequency-guided selective gate vector. Our experiments show that in terms of RMSE, HCSC performs significantly better when compared with on famous datasets, despite having less complexity, and thus can be trained much faster. More importantly, our model performs significantly better than previous models when the training data is sparse and has cold-start problems.

本文介绍了Hybrid Contextualized Sentiment Classifier (HCSC)，包含两个模块：(1)快速单词编码器;(2) 冷启动感知注意力机制。门控向量通过频率指导选择，使得共享向量如有必要就可以取代原始向量。实验证明，该模型在冷启动问题和稀疏训练数据处理上性能显著优于既有模型，且具有更少的复杂度，因此训练速度更快。

针对情感分类的冷启动用户和产品关注