基于门控循环单元和胶囊特征的文本情感分析

针对简单的循环神经网络（RNN）无法长时间记忆信息和单一的卷积神经网络（CNN）缺乏捕获文本上下文语义的能力的问题，为提升文本分类的准确率，提出一种门控循环单元（GRU）和胶囊特征融合的情感分析模型G-Caps。首先通过GRU捕捉文本的上下文全局特征，获得整体标量信息；其次在初始胶囊层将捕获的信息通过动态路由算法进行迭代，获取到表示文本整体属性的向量化的特征信息；最后在主胶囊部分进行特征间的组合以求获得更准确的文本属性，并根据各个特征的强度大小分析文本的情感极性。在基准数据集MR上进行的实验的结果表明，与初始卷积滤波器的CNN（CNN+INI）和批判学习的CNN（CL_CNN）方法相比，G-Caps的分类准确率分别提升了3.1个百分点和0.5个百分点。由此可见，G-Caps模型有效地提高了实际应用中文本情感分析的准确性。

Text sentiment analysis based on gated recurrent unit and capsule features

Aiming at the problems that simple Recurrent Neural Network (RNN) cannot memorize information for a long time and single Convolutional Neural Network (CNN) lacks the ability to capture the semantics of text context, in order to improve the accuracy of text classification, a sentiment analysis model G-Caps (Gated Recurrent Unit-Capsule) was proposed, which combines Gated Recurrent Unit (GRU) and capsule features. First, the contextual global features of the text were captured through GRU in order to obtain the global scalar information. Second, the captured information was iterated through the dynamic routing algorithm at the initial capsule layer to obtain the vectorized feature information representing the overall attributes of the text. Finally, the features were combined in the main capsule part to obtain more accurate text attributes, and the sentiment polarity of the text was analyzed according to the intensity of each feature. Experimental results on the benchmark dataset MR (Movie Reviews) showed that compared with the CNN + INI (Convolutional Neural Network + Initializing convolutional filters) and CL_CNN (Critic Learning_Convolutional Neural Network) methods, G-Caps had the classification accuracy increased by 3.1 percentage points and 0.5 percentage points respectively. It can be seen that the G-Caps model effectively improves the accuracy of text sentiment analysis in practice.