International Core Journal of Engineering 2020-26 | Page 57

2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Short-text Sentiment Classification based on Graph- LSTM Yuting Wan School of Control and Computer Engineering North China Electric Power University Beijing, China [email protected] A. RNN and LSTM The long and short memory neural network, often called LSTM, is a special RNN that learns long dependencies. They were introduced by Hochreiter & Schmidhuber and improved and popularized by many people. They work very well on a variety of issues and are now widely used LSTM is designed to avoid long dependency problems. Remember that longer historical information is actually their default behavior. All recurrent neural networks have the form of a repeating module chain of neural networks. In a standard RNN, the repeating module will have a very simple structure. Abstract—With the rapid development of social networks, short texts make it easier for people to comment on hot events and express their emotions. The information published by users through short texts contains emotional features of different trends, and it is of great significance to dig deep into these features. However, the traditional RNN algorithm, SVM and linear LSTM can only discriminate emotional sentiment because of the short text grammar and the sparse data, which is far from the purpose of opinion mining. Therefore, this paper proposes to apply Graph LSTM to short text classification, mine deeper information, and achieve good results. Finally, the paper compares three different machine learning methods to achieve fine-grained sentiment analysis. h 0 h 1 h 2 h 3 h 4 A A A A A X 0 X 1 X 2 X 3 X 4 Keywords—Graph-LSTM, Sentiment Classification, Short text I. I NTRODUCTION With the rapid development of Internet technology and its application, some new social media including Weibo, social networking sites, instant messaging, etc. are fundamentally changing the lives of human beings. However, due to the large number of users and the flood of information, each Weibo has a limited number of words, plus the use of many online languages or symbols, how to discover social sensation, hot topics and sensation about national security from Weibo. Trends, how to find people's evaluation of certain events or commercial products have become the focus of research. Among them, sentiment analysis in short text natural language processing is one of the key topics. The main purpose of short text sentiment analysis is to identify subjective information from short text information and to mine the opinions and attitudes held by users on product, news, hot events and other commentary information, provide a basis for enterprises to understand the user's consumption concept and improve product quality. Secondly, as a social manager or government, relying on sentiment analysis technology to explore the opinions and emotions of Weibo users on certain hot events or certain policies, we can understand the focus of public opinion, improve the government's policies and policies, and prevent sudden incidents. Real-time analysis of information has important reference value. Fig. 1. Linear LSTM propagation B. Graph LSTM In Graph LSTM, the back propagation method is different from the general linear structure LSTM, so the propagation mode needs to be redefined. For linear LSTM, parameter updates propagate from left to right or right to left along the chain structure. For Graph LSTM, parameter updates are propagated along the graph structure formed by the nodes. Traditional LSTM is essentially a deep feedforward neural network. For example, from left to right there is a hidden linear vector corresponding to each word, which is generated by the basic loop unit of the neural network, as the word embedding of the given word and the hidden vector of the previous word. In discriminative learning, these hidden vectors update the gradient in the entire network by backpropagation and finally input to the end classifier. Extending this strategy to the graph, in essence, is backpropagation according to the graph structure through the feedforward neural network, and the calculation result of each node is propagated to the adjacent node. The nodes on the map are composed of one case. The relationship between the plot of the similar case and the sentence of the trial requires a large amount of data to be accurate, and the nodes are linked to form a graph structure. Similar to the gradient backpropagation method, there is Confidence Propagation (LBP). However, the II. R ELATED RESEARCH According to the different methods of classification knowledge acquisition, the text automatic classification system can be divided into two types: classification system based on knowledge engineering and classification system based on statistics. 978-1-7281-4691-1/19/$31.00 ©2019 IEEE DOI 10.1109/AIAM48774.2019.00014 35