Aspect-level sentiment analysis method based on dependency syntax tree and deep learning

Abstract

The invention provides an aspect-level sentiment analysis method based on a dependency syntax tree and deep learning. The method comprises the steps of performing word segmentation and embedding training processing on a to-be-analyzed text to obtain word vector expression of each word in the text, and inputting the word vector expression into a bidirectional long-short-term neural network to obtain integrated information of each word and the text; constructing a dependency syntax tree by using all words in the text, constructing a GCN graph by using the dependency syntax tree, and performing iterative processing on the integrated information of the text and the GCN graph to obtain dependency syntax tree representation of an evaluation object in the text; and combining the integrated information of the text with the dependency syntax tree representation of the evaluation object, and performing analysis processing on a combined result by using a CNN to obtain an emotion prediction resultof the text for the evaluation object. According to the method, the semantic information of the text is effectively utilized, meanwhile, the importance of the evaluation object in aspect-level sentiment analysis is emphasized, and the accuracy of aspect-level sentiment analysis tasks of known evaluation objects is improved.

Description

technical field [0001] The invention relates to the technical field of natural language, in particular to an aspect-level sentiment analysis method based on dependency syntax tree and deep learning. Background technique [0002] LSTM (Long short-term memory, long short-term memory) is a special recurrent neural network, mainly to solve the problem of gradient disappearance in the long sequence training process of ordinary recurrent neural networks. LSTM can only predict the output of the next moment based on the timing information of the previous moment, but for some problems, the output of the current moment is not only related to the previous state, but also may be related to the future state, so there is a two-way long short-term memory network. Namely Bi-LSTM. Bi-LSTM saves the output values ​​of two LSTMs, one is the output of the hidden layer of the forward LSTM, and the other is the output of the hidden layer of the reverse LSTM. By tracking the word order relationsh...

AI Technical Summary

Problems solved by technology

Aspect-level sentiment analysis is fundamentally a natural language processing task, and deep learning is just a tool. Current research focuses too much on improving deep learning algorithms, while ignoring the basic information of natural language processing.

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