Multi-label text classification method based on BiGRU and attention mechanism

Abstract

The invention provides a multi-label text classification method based on BiGRU and an attention mechanism. The method comprises the following steps: S1, acquiring a plurality of web texts; s2, preprocessing the plurality of web texts; s3, extracting deep information features of the web text by using the pre-trained word vector; s4, adding corresponding weights to the deep information features according to an attention mechanism; s5, performing probability classification of different types of labels on the data obtained in the step S4 by using BiGRU; and S6, outputting the probability of each web text on different types of labels. The method has the advantages that the pre-training word vector is adopted to further shorten the training time, the attention mechanism is adopted to enable theneural network to focus on important information for improving the classification effect, and compared with the prior art, the BiGRU and the attention mechanism are fused to enable the method to obtain the same high accuracy by using less training time.

Description

technical field [0001] The present invention relates to the field of network text classification, in particular to a multi-label text classification method based on BiGRU and attention mechanism. Background technique [0002] Emotion recognition is one of the important topics in natural language processing. Today, when the Internet is highly developed, people post online through Weibo, news websites, forums, etc. The length of these speeches is variable, the vocabulary is not limited, and there are no strict grammatical rules, which have a strong subjective tendency. Among them, negative speech is an important topic that needs urgent attention. If the emotion of speech cannot be correctly identified, it will be impossible to prevent the occurrence of cyber violence in time and prevent behaviors that endanger the reputation of individuals and even enterprises. In this context, grid-related network text emotion recognition has high research significance. [0003] Text emotio...

AI Technical Summary

Problems solved by technology

The arrival of the Internet era has promoted the birth of many new words, which has a great impact on the sentiment classification model based on the emotional polarity dictionary. The existing emotional polarity dictionary is limited, and the model cannot effectively identify newly generated words or popular words.

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