A text emotion analysis method based on attention mechanism

Abstract

The invention discloses a text emotion analysis method based on attention mechanism, which comprises the following steps: 1, preprocessing text data; 2, constructing a vocabulary and constructing a word vector by using a GloVe model; Thirdly, the sentence vector is encoded by intrinsic attention, and the target word vector is encoded by interactive attention. The two encoded vectors are fused by GRU, and the fused representation is obtained after average pooling. Fourthly, according to the fusion representation, the abstract feature of context vector is obtained by point-by-point feed-forwardnetwork (FFN), and then the probability distribution of affective classification labels is calculated by full connection and Softmax function, and the classification result is obtained. Fifthly, the preprocessed corpus is divided into training set and test set, the parameters of the model are trained many times, and the model with the highest classification accuracy is selected to classify the affective tendencies. The method of the invention only uses the attention mechanism to model the text, and strengthens the understanding of the target word, so that the user can understand the emotionaltendency held by the specific target word in the text.

Description

technical field [0001] The invention relates to the field of text sentiment analysis, and more specifically, relates to a text sentiment analysis method based on an attention mechanism. Background technique [0002] In the Internet era of information explosion, social platforms such as social networks and instant messaging platforms have developed rapidly, becoming one of the important ways for network users to communicate and communicate, and also one of the largest information generation platforms on the Internet. Using the massive text data of social networks for sentiment analysis tasks will help provide more help in accurate product recommendations, criminal tracking, and public opinion monitoring and guidance. [0003] Target-level sentiment classification, which aims to determine the sentiment orientation of a sentence towards a specific target word, is a fine-grained sentiment analysis task that aims to determine the sentiment polarity (negative, neutral, or positive...

AI Technical Summary

Problems solved by technology

However, RNN is not computationally efficient and is not good at modeling long-term dependencies. It is not very effective in solving long-range dependencies, and its ability to model complete context information is limited.

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