Interacted grid tagging for sentiment word extraction

With the improvement of people’s quality of life, the requirements for service quality have become higher. The judgment of merchants on users' reviews has been improved from good or bad to specific aspects, while traditional sentiment classification is difficult to complete this task, where sentiment word extraction of text is the key. Most of the existing models use segmented subtasks for training. In order to solve the problem of model error propagation in multiple subtasks and improve the deep learning of data, an aspect sentiment analysis model based on interactive grid tagging scheme of bidirectional encoder representation from transformers (BERT) (IGTS-BERT) was proposed. Firstly, the rotation position encoding was used to enhance the model’ s sensitivity to position, and the word pairs were labeled with a low-dimensional method to improve the learning efficiency. Then, the two grid tagging networks based on the rotational position were made to interact, giving the model a better generalization ability. Finally, in order to further exert the effect of interacting, two data augmentation methods were proposed: splicing method and a method about random matching of aspect words and sentiment words, which further improves the performance of the model. The Triplets extraction and Pairs extraction tasks were tested on 4 standard datasets, with an average improvement of more than 4% in F1 value and a maximum improvement of 7%. Experimental results show that the IGTS-BERT model exhibits superior performance on sentiment word extraction.