Text sentiment classification is an essential research field of natural language processing. Recently, numerous deep learning-based methods for sentiment classification have been proposed and achieved better performances compared with conventional machine learning methods. However, most of the proposed methods ignore the interactive relationship between contextual semantics and sentimental tendency while modeling their text representation. In this paper, we propose a novel Interactive Dual Attention Network (IDAN) model that aims to interactively learn the representation between contextual semantics and sentimental tendency information. Firstly, we design an algorithm that utilizes linguistic resources to obtain sentimental tendency information from text and then extract word embeddings from the BERT (Bidirectional Encoder Representations from Transformers) pretraining model as the embedding layer of IDAN. Next, we use two Bidirectional LSTM (BiLSTM) networks to learn the long-range dependencies of contextual semantics and sentimental tendency information, respectively. Finally, two types of attention mechanisms are implemented in IDAN. One is multihead attention, which is the next layer of BiLSTM and is used to learn the interactive relationship between contextual semantics and sentimental tendency information. The other is global attention that aims to make the model focus on the important parts of the sequence and generate the final representation for classification. These two attention mechanisms enable IDAN to interactively learn the relationship between semantics and sentimental tendency information and improve the classification performance. A large number of experiments on four benchmark datasets show that our IDAN model is superior to competitive methods. Moreover, both the result analysis and the attention weight visualization further demonstrate the effectiveness of our proposed method.

中文翻译：

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交互式双注意力网络的文本情感分类

文本情感分类是自然语言处理的重要研究领域。最近，已经提出了许多用于情感分类的基于深度学习的方法，并且与传统的机器学习方法相比，它们具有更好的性能。但是，大多数提出的方法在对文本表示进行建模时都忽略了上下文语义和情感倾向之间的交互关系。在本文中，我们提出了一种新颖的交互式双重注意网络（IDAN）模型，该模型旨在以交互方式学习上下文语义和情感趋势信息之间的表示。首先，我们设计了一种算法，该算法利用语言资源从文本中获取情感倾向信息，然后从BERT（变压器的双向编码器表示）预训练模型中提取单词嵌入作为IDAN的嵌入层。接下来，我们使用两个双向LSTM（BiLSTM）网络分别学习上下文语义和情感趋势信息的远程依赖关系。最后，在IDAN中实现了两种类型的注意力机制。一种是多头注意力，它是BiLSTM的下一层，用于学习上下文语义和情感趋势信息之间的交互关系。另一个是全球关注，其目的是使模型专注于序列的重要部分并生成最终的分类表示。这两种注意机制使IDAN可以交互地学习语义和情感倾向信息之间的关系，并提高分类性能。在四个基准数据集上进行的大量实验表明，我们的IDAN模型优于竞争方法。此外，结果分析和注意权重可视化都进一步证明了我们提出的方法的有效性。