News text oriented social emotion sorting method based on hierarchical state neural network

A neural network and sorting method technology, applied in the field of social emotion sorting based on hierarchical state neural network, can solve the problems of not considering the word order bag model, difficulty in capturing long-distance dependencies, and not making full use of document semantic structure information, etc. The effect of improving performance

Pending Publication Date: 2020-06-26
SOUTHEAST UNIV
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However, existing research on related sentiment ranking methods is usually based on shallower representations, such as bag-of-words models that do not co

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  • News text oriented social emotion sorting method based on hierarchical state neural network
  • News text oriented social emotion sorting method based on hierarchical state neural network
  • News text oriented social emotion sorting method based on hierarchical state neural network

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[0031] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

[0032] The problem can be described as follows: Suppose there are T emotional labels E={e 1 ,e 2 ,...,e T} and K documents D = {d 1 , d 2 ,...,d K}, each document d i has its associated sentiment sorted set and unrelated set of emotions Relevant sentiment ranking aims to learn a score function g(d i )=[g 1 (d i ),g 2 (d i ),…, g T (d i )] for each emotion e j (j=1,2,...,T) assign a score g j (d i ). In order to distinguish relevant emotions from i...

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Abstract

The invention discloses a news text oriented social emotion sorting method based on a hierarchical state neural network, which pays attention to semantic hierarchical structure information of documents and solves related emotion sorting problems in social emotion detection. The method comprises the steps of preprocessing a news text; encoding the words through a sentence state recurrent neural network to obtain sentence representation; encoding sentences through a document state recurrent neural network to obtain document representation; and on the basis of document representation, mapping byusing a multi-layer perceptron, and normalizing softmax to obtain a sorting result of related emotions. Compared with a previous related emotion sorting method, the method has the advantages that thehidden states of all words or sentences are coded at the same time in each time step, and long-distance semantic dependence can be better captured. Besides, a hierarchical structure mechanism is adopted to capture a key hierarchical semantic structure in the document, an important part for evoking emotions in the document is dynamically highlighted, and the performance of related emotion sorting can be improved.

Description

technical field [0001] The invention relates to using a computer to perform emotion detection on text, in particular to a news text-oriented social emotion sorting method based on a hierarchical state neural network, which belongs to the technical field of machine learning. Background technique [0002] Text sentiment analysis is the process of analyzing, processing, summarizing and inferring texts with emotions or that may arouse readers' emotions. With the continuous development of the Internet, people have become accustomed to expressing their views, ideas and attitudes online. Understanding people's emotional state has important practical significance for some applications, such as dialogue systems and recommendation systems. Social sentiment detection refers to predicting the emotional responses evoked by news texts on the Internet in society, usually expressed as the distribution of sentiments. Social sentiment detection is of great significance to public opinion con...

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Application Information

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IPC IPC(8): G06F16/9536G06F40/211G06N3/04
CPCG06F16/9536G06N3/044
Inventor 周德宇张朦杨扬
Owner SOUTHEAST UNIV
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