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
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[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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