A fine-grained emotion polarity prediction method based on a hybrid attention network

A technology of emotional polarity and prediction method, which is applied in the direction of instruments, computing, and electrical digital data processing, etc., can solve problems such as insufficient accuracy, long model training time, and insufficient flexibility, so as to make up for insufficient accuracy and effective emotional polarity Effect

Active Publication Date: 2019-06-28
JILIN UNIV
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Problems solved by technology

[0011] The technical problem to be solved by the present invention is to overcome the problems of insufficient flexibility, insufficient precision, long model training ti

Method used

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  • A fine-grained emotion polarity prediction method based on a hybrid attention network
  • A fine-grained emotion polarity prediction method based on a hybrid attention network
  • A fine-grained emotion polarity prediction method based on a hybrid attention network

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Embodiment

[0101] The task that the present invention needs to solve is as follows: given length is the sentence s={w 1 ,w 2 ,···,a 1 ,a 2 ,···a m ,···w n}, that is, each sentence consists of a sequence of words w i (1≤i≤n) composition, where a j(1≤j≤m) is a specific aspect target word in the sentence s. The task of the present invention is to predict the emotional polarity of the specific aspect target word in the sentence according to a given sentence, including positive, negative and neutral.

[0102] The specific execution flow of the present invention is as follows for above-mentioned tasks:

[0103] (1) According to the given sentence, get the text context word sequence and specific aspect target word sequence:

[0104] S c ={w 1 ,w 2 ,...w n}

[0105] S a ={a 1 , a 2 ,...a m}

[0106] (2) Segment the given sentence according to the number of target words in a specific aspect:

[0107] sentence

aspect-specific target words

{w 1 ,w 2 ,...w ...

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Abstract

The invention discloses a fine-grained emotion polarity prediction method based on a hybrid attention network, and aims to overcome the problems of lack of flexibility, insufficient precision, difficulty in obtaining global structure information, low training speed, single attention information and the like in the prior art. The method comprises the following steps: 1, determining a text context sequence and a specific aspect target word sequence according to a comment text sentence; 2, mapping the sequence into two multi-dimensional continuous word vector matrixes through log word embedding;3, performing multiple different linear transformations on the two matrixes to obtain corresponding transformation matrixes; 4, calculating a text context self-attention matrix and a specific aspect target word vector attention matrix by using the transformation matrix, and splicing the two matrixes to obtain a double-attention matrix; 5, splicing the double attention matrixes subjected to different times of linear change, and then performing linear change again to obtain a final attention representation matrix; and 6, through an average pooling operation, inputting the emotion polarity into asoftmax classifier through full connection layer thickness to obtain an emotion polarity prediction result.

Description

technical field [0001] The present invention relates to a specific aspect fine-grained emotional polarity prediction method in the field of natural language processing, more precisely, the present invention relates to a fine-grained emotional polarity prediction method based on a hybrid attention network. Background technique [0002] The rapid development of social networks has provided people with a broad platform for expressing and sharing personal opinions. Various network data are rapidly expanding, and more and more people express their opinions and emotions on the Internet. When users express their views on a certain entity, except In addition to giving an overall evaluation in the comments, opinions and comments are usually made on multiple aspects of the entity. Identifying the emotions of different specific aspects of user comments will help users make better decisions. Therefore, identifying specific aspects of online comment texts On the one hand, emotional polar...

Claims

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Application Information

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IPC IPC(8): G06F17/27
Inventor 王英孙小婉王鑫孙玉东于尤婧凌云志马涪元
Owner JILIN UNIV
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