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Sentiment Analysis Method of Network Text Based on Improved AT-BiGRU Model

机译：基于改进的 - Bigru模型的网络文本情感分析方法

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摘要

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.
机译：为了解决现有的网络文本情绪分析方法中存在的问题，如长训练时间，复杂的计算和大空间成本，提出了一种基于改进的at-bigru模型的互联网文本情绪分析方法。首先，将导入TextBlob包以在文本预处理之前更正拼写错误。其次，PAD_SEQUENTS用于填充具有固定长度的输入层，双向门控复发网络用于提取信息，并且使用注意机制来突出显示单词矢量的关键信息。最后，改变了GNU存储器单元，并且构造了可以适应递归网络结构的改进的BIGRU。建议的模型在Semeval-2014任务4和Semeval-2017任务4数据集上进行了实验展示。实验结果表明，该建议的模型可以有效避免由拼写错误引起的文本情绪分析偏差，并在精度，损失率和迭代时间方面证明了改进的at-bigru模型的有效性。

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