Semantic fragment-oriented target emotion analysis method, device and equipment and medium

Abstract

The invention discloses a semantic-fragment-oriented target sentiment analysis method, device and equipment and a medium, and is applied to the field of target sentiment analys.The method comprises the steps that a text sample is input into an embedding layer, and a sentence word vector, a context word vector and a target word vector are obtained; encoding the sentence word vector, the context word vector and the target word vector by using an attention encoding layer to obtain a corresponding hidden state representation; inputting the hidden state representation of the context and the target word into a multi-head attention layer to obtain a target context semantic feature representation; inputting the sentence hidden state representation into a structured self-attention layer to obtain sentence segmentation semantic feature representation; and inputting the target context semantic feature representation and the sentence segmentation semantic feature representation into a prediction layer to obtain a target word sentiment prediction result. Therefore, according to the method, the model focuses on each semantic segment, so that the noise on words is inhibited, and the accuracy of the target sentiment analysis task is improved.

Description

technical field [0001] The present invention relates to the field of target sentiment analysis, in particular to a semantic segment-oriented target sentiment analysis method, device, device and medium. Background technique [0002] The Targeted Sentiment Analysis (TSA) task refers to judging the sentiment polarity of a specific target in a sentence. [0003] At this stage, the technology uses an attention mechanism to capture the relationship between a specific target in a sentence and other words / phrases in the sentence, that is, the weight. However, since the attention mechanism only pays attention to the relationship between words, it is prone to the wrong collocation of a specific target and other irrelevant words. For example, in the sentence "The bed is so good and so comfortable butornament of this room is really ugly.", the attention mechanism mistakenly assigns the same value to "good", "comfortable" and "ugly" before and after the target "ornament" weights, thus ...

AI Technical Summary

Problems solved by technology

However, since the attention mechanism only pays attention to the relationship between words, it is easy to make mistakes between specific targets and other irrelevant words.

For example, in the sentence "The bed is so good and so comfortable butornament of this room is really ugly.", the attention mechanism will mistakenly assign the same weight to "good", "comfortable" and "ugly" before and after the target "ornament". weight, thus causing the accuracy of the target sentiment analysis task to be affected

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