In order to solve the problems existing in the current method of emotional analysis of network text, such as long training time, complex calculation, and large space cost, this paper proposes an Internet text sentiment analysis method based on the improved AT-BiGRU model. Firstly, the textblob package is imported to correct spelling errors before text preprocessing. Secondly, pad_sequences are used to fill in the input layer with a fixed length, the two-way gated recurrent network is used to extract information, and the attention mechanism is used to highlight the key information of the word vector. Finally, the GNU memory unit is transformed, and an improved BiGRU that can adapt to the recursive network structure is constructed. The proposed model is experimentally demonstrated on the SemEval-2014 Task 4 and SemEval-2017 Task 4 datasets. Experimental results show that the proposed model can effectively avoid the text sentiment analysis bias caused by spelling errors and prove the effectiveness of the improved AT-BiGRU model in terms of accuracy, loss rate, and iteration time.

中文翻译：

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基于改进的AT-BiGRU模型的网络文本情感分析方法

为了解决当前网络文本情感分析方法存在的训练时间长，计算复杂，空间成本大等问题，提出了一种基于改进的AT-BiGRU模型的互联网文本情感分析方法。首先，导入textblob包以在文本预处理之前纠正拼写错误。其次，使用pad_sequences填充固定长度的输入层，使用双向门控递归网络提取信息，并使用注意力机制突出单词向量的关键信息。最后，对GNU存储单元进行了转换，构造了一种可以适应递归网络结构的改进BiGRU。在SemEval-2014 Task 4和SemEval-2017 Task 4数据集上通过实验证明了所提出的模型。