## 1 Introduction  
With the rapid development of information technology, the Internet has become an indispensable part of people's lives, and people increasingly like to leave their own reviews on various platforms. Effective use of these review text data, mining and analyzing the sentiment tendencies they contain, has important practical application value; for example, reviews in the e-commerce platform have important reference value for other users to choose commodity, and also help merchants improve their products and services. As one of the most popular social media platforms worldwide, Twitter contains views, opinions, and ideas on a variety of topics, and in the context of recent works related to Twitter data mining and analysis, a number of works have focused on the sentiment analysis of tweets, which can provide effective information for various fields [1, 2].  
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task; its purpose is to identify the different aspects of a text and judge their corresponding sentiment polarity [3]. ABSA can be divided into two subtasks: aspect terms extraction (ATE) and aspect sentiment classification (ASC) [4]. The purpose of end-to-end ABSA is to complete the two subtasks of ATE and ASC at the same time to achieve the complete ABSA process. Since the ATE task is a sequence-labeling task and the ASC task is a classification task, in order to make the end-to-end model solve the two tasks together, the current research methods are to treat the ASC task as a sequence-labeling task [5].  
In the initial study, each subtask was considered separately and was not linked with other tasks [5, 6]. Recently, more and more research has begun to focus on the connection between two subtasks and deal with them together by analyzing the connection between the two subtasks [7; 8; 9]. However, they do not take into account that there is also a connection between the aspects, such as \"nice operating system and keyboard\", the sentiment polarity of the aspect term \"operating system\" is determined by \"nice\", and \"operating system\" and \"keyboard\" are connected by the juxtaposition \"and\", then the sentiment polarity of these two aspects should be the same and both positive. However, it may be affected by the context and labeled as a different sentiment. Secondly, \"operating system\" is an aspect item composed of two words; because we treat the aspect sentiment classification as a sequence-labeling task, then there may be a multi-word aspect item with two different sentiment polarities, for example, \"operating system\" may be labeled as \"B-POS E-NEU\", which appears as an aspect item that contains two different sentiment polarities, which is obviously unreasonable.  
In response to the above problems, an interactive learning network that maintains sentiment consistency is proposed. In this network, the task-sharing layer is used to link the two subtasks preliminarily; then, the multi-head attention mechanism is used to combine the context information to let the model consider the connection between the aspects and realize the interaction between the two subtasks so that the model can fully interact with information and consider the context information of the text. An auxiliary component with a gate mechanism is also designed, which can be used to maintain sentiment consistency within aspect items by considering the results of the previous moments at the current moment and correcting the current results.  
Our main purpose and contributions are summarized as follows:  
* A new framework is proposed to address the complete ABSA in an end-to-end manner. Use the task-sharing layer to enable interaction between two subtasks and take advantage of the multi-head attention mechanism to consider the connection between aspect items;
* An auxiliary component with a gate mechanism is designed to maintain sentiment consistency within aspect items.