Aspect-level text sentiment classification method and system

Abstract

The invention discloses an aspect-level text sentiment classification method and system, and the method comprises the steps: extracting the long-distance dependence features of a sentence text according to the obtained local feature vectors of the sentence text, and obtaining the context feature representation of the sentence text; constructing a syntactic dependency relationship among words in the sentence text according to the context feature representation of the sentence text to obtain aspect-level feature representation of the sentence text; and constructing a dependency tree-based graphattention neural network, and obtaining aspect-level emotion categories of the text according to aspect-level feature representation of the sentence text. The method comprises the steps of extractinglocal feature information in a sentence by adopting a convolutional neural network, learning pooled features of the convolutional neural network by utilizing a bidirectional long-short-term memory network, obtaining context information of the sentence, constructing a dependency tree-based graph attention network model, and modeling a sentence dependency relationship by utilizing syntactic information of a dependency tree, thereby improving the performance of sentiment classification.

Description

technical field [0001] The invention relates to the technical fields of natural language processing and deep learning, in particular to an aspect-level text sentiment classification method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Sentiment analysis is one of the most active research fields in natural language processing and an important task in text mining, also known as opinion mining. Most sentiment analysis work is at the text level and sentence level. Since the sentiment expressed by a word in different environments may be opposite, aspect-level sentiment analysis is used. [0004] Aspect-level sentiment analysis is a fine-grained task in the field of sentiment classification. Its goal is to predict the sentiment polarity of aspects appearing in a given text based on a given opinion sentence and evaluation aspect, c...

AI Technical Summary

Problems solved by technology

[0007] However, the inventors found that although attention-based models have achieved good experimental results in many tasks, they are not enough to capture the syntactic dependencies between contextual words and aspects in sentences, and the attention module may suffer from grammatical Some irrelevant words will be highlighted due to the lack of missing, and some valuable and important information will be lost. Therefore, there are obvious limitations in solving the dependency problem between multiple words in aspect-level sentiment analysis.

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