A natural language emotion analysis method based on a deep network

Abstract

The invention provides a natural language emotion analysis method based on a depth network. On the basis of a memory network, semantic dependent information is introduced to guide the execution of theattention mechanism, and the context moment information including the whole emotion information of a sentence is also used to provide background information for the current analysis object word. Thewhole model includes an embedding module, a memory sequence building module, a semantic dependency mask attention module, a context moment affective learning module and an output module. In the model,the semantic dependency information of object words and context obtained from dependency syntax tree is introduced into the memory network, so that the memory sequences of each layer are generated dynamically, which leads to the execution of attention mechanism in the multi-layer module of the memory network. In addition, in order to introduce the whole affective information of a sentence, that is, the relationship information between all the object words in the same sentence, we propose a context-based learning task, which assists the affective analysis of specific object words by multitasking learning.

Description

technical field [0001] The present invention relates to the field of sentiment analysis in computer natural language processing, and more specifically, relates to a deep network-based natural language sentiment analysis method. Background technique [0002] The main goal of the object-level sentiment analysis task is to give the sentiment polarity (such as positive, negative, or neutral) of each evaluation object in the sentence for one or more evaluation objects existing in a given sentence. For example, given the sentence "the price of this restaurant is very cheap, but the service is poor" and the evaluation target "price" and "service", for the evaluation object "price", the sentiment polarity is positive, while for the evaluation object "service" , and the emotional polarity is negative. Obviously, for different object words in the same sentence, the sentiment analysis results may be different. [0003] As the attention mechanism and memory network have achieved good ...

AI Technical Summary

Problems solved by technology

[0008] For the existing mainstream methods based on attention mechanism and memory network, only the positional relationship between the object word and the context is considered when generating the memory sequence, and the semantic connection between the object word and the context is not considered. The present invention Propose a kind of natural language emotion analysis method based on depth network, the technical scheme that the present invention adopts is:

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