Target emotion analysis method and system based on attention gated convolutional network

Abstract

The invention discloses a target sentiment analysis method and system based on an attention gated convolutional network, and the method comprises the steps: step 1, inputting a given context word vector and a corresponding target word vector, and enabling the given context word vector and the corresponding target word vector to serve as inputs for training; step 2, performing multi-head attentionmechanism interaction by utilizing the context words and the context sensing target words; step 3, enabling the sentiment feature vectors cintra and tinter generated by two channels to respectively pass through a gating convolution mechanism to generate a context word representation ai and a context word representation ui with context perception target word representations; step 4, pooling the emotion feature oi, and selecting the most representative feature; step 5, performing full connection on the pooled feature word vectors, and then performing classification through a Softmax classifier;and step 6, training and updating the attention gated convolutional network model by minimizing the cross entropy loss function. The accuracy can be effectively improved, the convergence time can be shortened, and the practicability is higher.

Description

technical field [0001] The invention is applied to the field of target emotion analysis, and in particular relates to a target emotion analysis method and system based on attention-gated convolutional network. Background technique [0002] Target sentiment analysis is one of the sentiment classification tasks, which classifies the sentiment polarity mentioned by each target entity in a given text, and is a current research hotspot. The target entity exists in a given text, and a text can have multiple target entities. Target-based sentiment analysis is a fine-grained sentiment classification task. When multiple entities in the text have different sentiment polarities, target sentiment analysis can classify the sentiment polarity of an entity in the text. In the text, the emotional polarity of different entities corresponding to the text may be opposite. For example, "I bought a mobile phone that looks great, but the battery life is a bit short". There are two target entit...

AI Technical Summary

Problems solved by technology

[0007]Based on the above target sentiment analysis, the RNN model is usually used, which leads to long training time and other alternative models fail to achieve good interaction between the context and the target word.

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