# Multiple Interactive Attention Networks for Aspect-Based Sentiment Classification
## 2 Related Works  
In this section, we have introduced the main research methods of ABSC, including traditional machine learning methods and neural networks methods. Traditional machine learning methods [9] focus on text representation and feature extraction. It can extract a series of features, such as sentiment lexicon features and bag-of-words features, to train the sentiment classifier. The most commonly used classification methods include K-Nearest Neighbor (KNN) [10], naive Bayesian model [11], SVM. However, these methods rely heavily on the characteristics of manual extraction and require a lot of manpower and may be unable to achieve satisfactory results when the dataset changes. Therefore, the generality of these methods is poor, and they are difficult to be applied to other datasets. The semi-supervised model solves these problems to a large extent. It is successfully used to detect the Internet of Things (IoT) distributed attack [12] and plays a significant role in maintaining social network security [13]. Recent work is being combined with neural networks because of the ability to capture the original features, which can be mapped to continuous and low-dimensional vectors without the need for feature engineering. Because of these advantages, much more structured neural networks have been derived, such as Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Networks (GAN), which are used to solve problems in NLP(Natural Language Processing). These methods based on neural networks have attracted much attention, especially Long Short-Term Memory (LSTM). In order to effectively model the semantic relevance of the target words and the context in the sentence, [PERSON] et al. [14] proposed Target-Dependent Long Short-Term Memory (TD-LSTM). TD-LSTM uses two LSTMs to model around the target from the left and right to take the context as the feature representation of sentiment classification. Then, [PERSON] et al. proposed Target-Connection Long Short-Term Memory (TC-LSTM) based on TD-LSTM to enhance the interaction between target words and context. Attention mechanism [15], which obtains good results in machine translation tasks, guides the model to get a small amount of important information from a large amount and focuses on it. [PERSON] et al. [16] proposed a method called ATAE-LSTM(Attention-based Long Short-Term Memory with Aspect Embedding), which combines attention-based LSTM with aspect embedding; in this method, it combines the attention mechanism with LSTM to model sentences semantically; by doing this, it could be addressing the problem of aspect-based sentiment analysis. [PERSON] et al. [17] proposed a model using a deep memory network and attention mechanism on the ABSC, and this model is composed of a multi-layered computational layer of shared parameters; each layer of the model incorporates positional attention mechanism, and this method could learn a weight for each context word and use this information to calculate the text representation. [PERSON] et al. [18] proposed Interactive Attention Networks (IAN), which interactively learn attention in the context words and target words. They used the attention mechanism to link the target word to the context word for multi-level semantic classification. [PERSON] et al. [19] proposed a Position-Aware Bidirectional Attention Network (PBAN), which not only concentrates on the position information of aspect terms but also mutually models the relation between aspect terms and sentence by employing bidirectional attention mechanism. [PERSON] et al. [20] addressed the problems of strong mode over-learning and weak mode under-learning in the neural network learning process and proposed a progressive self-supervised attention mechanism algorithm. They properly constrained the attention mechanism.