Specific target emotion classification method based on graph neural network

Abstract

The invention relates to a specific target emotion classification task based on a graph neural network. The method comprises the following steps: acquiring a data set and initializing a BERT model; obtaining a one-dimensional feature vector of each target word through a BERT model; inputting the feature vector of the target word into a graph convolutional neural network model; constructing a network topological graph, calculating an adjacency matrix, obtaining three features of nodes in the network topological graph in three modes according to the adjacency matrix, introducing relation classification tasks,wherein the whole model is divided into two stages and two tasks in classification, and the two tasks are emotion polarity classification of target subjects and relation classificationbetween the target subjects respectively. According to the method, the graph neural network is adopted to compose a plurality of subjects appearing in sentences and process a plurality of targets at the same time, so that the cognitive law of judging emotion polarity by human beings is better met, the effect of a model is ensured, meanwhile, a relationship classification task is introduced for auxiliary classification, and the classification accuracy is further improved.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a specific target emotion classification method based on a graph neural network. Background technique [0002] With the emergence of various Internet new media forms and the development of e-commerce platforms, the growing user groups have produced massive user-generated content while extensively participating in network activities. In this context, it is particularly important to conduct accurate sentiment analysis and opinion mining for massive subjective texts on the Internet, and fine-grained sentiment analysis has developed rapidly in recent years. Fine-grained sentiment analysis aims to mine the different emotional tendencies of users towards different target subjects in a comment. In practical application scenarios, fields such as fine product models, user portraits, and personalized recommendations have great application prospects. [0003] The task of determin...

AI Technical Summary

Problems solved by technology

[0005] The method based on the sentiment dictionary mainly judges the sentiment polarity of the text by constructing a sentiment dictionary and a series of rules, relying on some existing sentiment dictionaries and domain dictionaries and a series of rules to build a sentiment classifier, but the maintenance of the sentiment dictionary requires It consumes a lot of manpower and material resources, and with the continuous emergence of new words, it can no longer meet the application requirements, and needs to be improved and optimized urgently

Or domain experts select a set of statistically significant features from the text, and then use machine learning methods to build a classification model to identify the emotional polarity of the text. Common classification models include naive Bayesian, maximum entropy, and support vector machines, etc. , but the shortcomings are: for different data sets, experts need to select different features, and the investment cost is relatively high. Different feature selection methods will make the final classification results vary greatly, and there may also be differences in different data sets. Large variance in performance, poor generalization of the model

The disadvantage is that the word vectors trained by the Word2Vec and Glove models are static word vectors, which fail to take into account the different meanings expressed by the same word in different contexts and contexts.

At present, the BERT model is rarely used in specific target emotion classification tasks, and in the existing methods, when there are multiple target subjects in a sentence, when the existing method is used to perform emotional classification on a specific target, the model processes the multi-target The emotion classification task is split into single-target emotion classification tasks for processing, ignoring the inherent correlation and connection between different target subjects in the same sentence

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