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.
