Sentiment analysis is designed to classify documents into a fixed number of pre-defined categories that represent different sentiments. Focusing on the limitation of insufficient training data, multi-task learning models based on deep learning have recently achieved significant progress in this field. In general, these models leverage multiple datasets annotated for different tasks to improve the performance on each individual dataset. The improvement is particularly evident on tasks with limited training data. However, most of these models suffer from two limitations. First, they use the final output of the hidden layer as the overall representation of the text, which initially loses a certain amount of semantic information. Second, although some of them utilize a certain gate mechanism to select shared features, some irrelevant shared features are erroneously used owing to polysemy. To address these two limitations, we integrate a pooling layer into a Bi-directional Recurrent Neural Network (BRNN) to extract semantic information comprehensively. We then apply the attention mechanism between shared layers and task-specific layers to identify the effective shared features, and propose an Attention-based Separate Pooling BRNN (ASP-BRNN) model. We conduct experiments to show the effectiveness of our models on four datasets (SST1, SST2, SUBJ, and IMDB), and the accuracy of our models increases steadily by approximately 0.5% for each model. It proves the effectiveness of every newly added component in solving the two problems. A further evaluation on eight datasets shows our proposed ASP-BRNN model outperforms current state-of-the-art models, such as ASP-MTL model (at least +0.2% on Electronics and at most +6.9% on IMDB), MT-ARC-II model (at least +0.2% on SST2 and at most +3.8% on DVDs).

情感分析旨在将文档分类为代表不同情感的固定数量的预定义类别。专注于训练数据不足的局限性，基于深度学习的多任务学习模型最近在该领域取得了重大进展。通常，这些模型利用标注有不同任务的多个数据集来提高每个单独数据集的性能。对于训练数据有限的任务，这种改进尤为明显。但是，这些模型中的大多数都有两个局限性。首先，他们使用隐藏层的最终输出作为文本的整体表示，该文本最初会丢失一定数量的语义信息。其次，尽管其中一些利用某种门机制来选择共享特征，由于多义性，一些不相关的共享特征被错误地使用。为了解决这两个限制，我们将池化层集成到双向递归神经网络（BRNN）中，以全面提取语义信息。然后，我们在共享层和特定于任务的层之间应用注意力机制，以识别有效的共享功能，并提出基于注意力的单独池BRNN（ASP-BRNN）模型。我们进行实验以显示我们的模型在四个数据集（SST1，SST2，SUBJ和IMDB）上的有效性，并且每个模型的模型准确性均稳定地提高了约0.5％。它证明了每个新添加的组件在解决两个问题上的有效性。对八个数据集的进一步评估表明，我们提出的ASP-BRNN模型优于当前的最新模型，