# A Multi-Attention Network for Aspect-Level Sentiment Analysis
## 1 Introduction  
Text sentiment analysis, is the process of analyzing, processing, summarizing, and inferring subjective text with sentiment polarity, which is a vital task in natural language processing (NLP) and is also known as opinion mining [1]. With the rapid development of Internet technology and continuous popularity of social networks, people express their opinions on different platforms, such as online social media, product review websites and other media. Thus, accumulating a large amount of text information data. Relevant comments and opinions have become an important reference for users, and even in academic research, they are also interested in the processing of massive data. In recent years, aspect-level sentiment analysis (ASA) is proposed, which could understand reviews better than traditional sentiment analysis and aim at identifying the sentiment polarity of an input sentence in a certain aspect term [2]. For example, given the sentence \"Great food but service was dreadful\", the polarity of the sentence towards the aspect \"food\" is positive while the polarity of \"service\" is negative. Previously, most of the literatures make use of neural networks to capture the sequence information and automatically learn useful representations of context and aspect terms [3], such as Recurrent Neural Network (RNN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). [PERSON] et al. [4] proposed a method of target extraction based on the gated convolution network, which was easy to confuse the sentiment polarity of aspect terms and could not achieve better fusion with the original sentence. The transduction learning approach proposed by [PERSON] et al. [5] is able to extract aspect terms well, but it did not fully analyze the sentiment of a particular aspect term. However, the existing neural network models are not effective enough in dealing with ASA tasks alone, and they are still in the immature stage of processing fine-grained sentiment analysis tasks. It can be seen that most of the current models of ASA tasks adopt the method of combining neural networks and attention mechanisms. Attention is widely used in machine translation [6] and reading comprehension [7]. Therefore, a number of researchers have also used attention mechanisms to solve ASA tasks, which could focus on the effect of different contextual words on the target [8]. [PERSON] et al. [9] proposed the Memory Network (MemNet) model, which consists of computational layers of shared parameters. Each layer contains an attention layer and a linear layer. [PERSON] et al. [10] proposed Attention-based LSTM with Aspect Embedding (ATAE-LSTM), which was able to attend to different parts of a sentence when different aspects are concerned. From the experimental results of MemNet and ATAE-LSTM, they all show that the attention mechanism is effective. Considering that neural networks are difficult to parallellize and requires a great deal of computation, this paper chooses to use multiple attention mechanisms to complete the task. Currently, word embedding and aspect term embedding usually use the original data preprocessing methods, such as Glove [11] and Word2 vec. For example, [PERSON] et al. [12] proposed that context embedding and aspect term embedding be used for inputting for ASA, and Glove be used for preprocessing input data. For the ASA task and datasets proposed in this paper, we find the data preprocessed by Bidirectional Encoder Representations from Transformers (BERT) [13] will greatly improve the effect of downstream tasks. Therefore, BERT is adopted to preprocess data onto this paper. Through the experimental comparison of this paper, the method improves the experimental results effectively. Inspired by the above problems, we propose a Multi-Attention Network (MAN) model for ASA. This paper mainly completes the ASA task, which is a fine-grained sentiment analysis task, and the goal is to infer the corresponding sentiment polarity for given sentences with aspect terms, such as positive, negative, and neutral.