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Cross-domain emotion classification system based on attention mechanism fusion

A technology of emotion classification and attention, applied in text database clustering/classification, semantic analysis, computer components, etc., can solve problems such as large amount of calculation

Pending Publication Date: 2020-03-10
FUZHOU UNIV
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AI Technical Summary

Problems solved by technology

Among them, the soft attention mechanism is to give a probability for each word in the input sentence when seeking the probability distribution of attention distribution, and then pass it to the next layer; the hard attention mechanism is to find a specific word directly from the input play. words, and then align the target sentence word with this word, while the words in other input sentences are rigidly considered to have a probability of 0; the local attention mechanism is a combination of soft attention mechanism and hard attention mechanism. Always consider that there are many hidden layers in the previous encoding, so the amount of calculation is large

Method used

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  • Cross-domain emotion classification system based on attention mechanism fusion
  • Cross-domain emotion classification system based on attention mechanism fusion
  • Cross-domain emotion classification system based on attention mechanism fusion

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Embodiment Construction

[0019] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0020] The present invention provides a cross-domain emotion classification system based on attention mechanism fusion, including:

[0021] The text preprocessing module is used to obtain the vector form corresponding to the text in the source domain and the target domain;

[0022] Text semantic learning module, used for learning the semantic dependency between the words of the text vector obtained by the text preprocessing module;

[0023] The attention mechanism fusion module obtains the comprehensive weight of the words of the text vector to the text by fusing different attention methods;

[0024] The hierarchical attention module calculates the attention weight of the text from the word level and the sentence level respectively, judges the weight of the word to the sentence representation, and the weight of the sentence to the docume...

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Abstract

The invention relates to a cross-domain emotion classification system based on attention mechanism fusion. The system comprises a comment text preprocessing module used for obtaining vector forms of texts in a source domain and a target domain; a text semantic learning module which is used for learning a semantic dependency relationship between words; an attention mechanism fusion module which isused for fusing different attention modes to obtain comprehensive weights of words for text classification; a hierarchical attention module which is used for calculating attention weights of the textfrom a word level and a sentence level respectively and judging weights of words for sentence representation and sentences for document representation; and an emotion category output module which is used for obtaining a final emotion classification result by utilizing the classification function. According to the method, the potential universal features of the target domain and the source domain can be automatically extracted, the features are abstracted and combined, and finally the emotion category of the text of the target domain is recognized.

Description

technical field [0001] The present invention relates to the field of emotion analysis and opinion mining, and more specifically, to a cross-domain emotion classification system based on attention mechanism fusion, which can learn domain-adapted feature representations through cross-domain text representation learning, and better perform cross-domain Analysis of Domain Sentiment Categories. Background technique [0002] Sentiment classification is an important and challenging task. Remarkable success has been achieved in domains with sufficient labeled training data. However, labeling enough data is very time-consuming and labor-intensive, setting a significant barrier for adapting sentiment classification systems to new domains. At the same time, when users express emotions in different domains, they often use different words, if we directly apply a classifier trained in one domain to other domains, due to the differences between these domains, the resulting performance wi...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F40/205G06F40/289G06F40/30G06K9/62G06N3/04
CPCG06F16/35G06N3/045G06F18/214
Inventor 廖祥文陈癸旭陈志豪邓立明陈开志
Owner FUZHOU UNIV
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