A bidirectional LSTM model emotion analysis method based on attention enhancement

Abstract

The invention relates to a bidirectional LSTM model emotion analysis method based on attention enhancement. According to the method, an attention mechanism and a bidirectional LSTM model are combined;text semantic information is learned by using a bidirectional LSTM model; the attention mechanism is used for enhancing attention to key words, the method comprises the steps that firstly, input sentences are expressed through pre-trained word vectors, then expression is conducted through a bidirectional LSTM model and attention model learning, the vectors expressed through the two parts are spliced, and finally text sentiment analysis work is completed through a classifier. According to the method, the semantic information of the text is learned by using bidirectional LSTM; a self-attentionmechanism established on a word vector is used for enhancing the attention degree of emotion keywords in the sentence; A word vector attention mechanism and a bidirectional LSTM are of a parallel structure, experiments show that the model provided by the invention shows superior performance, and exceeds a known best model on a plurality of indexes, so that the requirements of practical applicationcan be well met.

Description

technical field [0001] The invention belongs to the technical field of text processing, and in particular relates to a sentiment analysis method based on an attention-enhanced bidirectional LSTM model. Background technique [0002] With the development of the Internet, the number of Internet users has increased sharply in recent years, and people have produced a large amount of valuable comment information on people, events, products, etc. in the process of information interaction. This information expresses people's various emotional colors and emotional tendencies. Through the mining of emotional information, users' behavior can be better understood, so as to predict the development direction or trend of events. However, with the huge expansion of the information scale, it is impossible to complete the sentiment analysis work only by manual work, so it is of great significance to use computers for efficient and accurate sentiment analysis work. [0003] At present, sentim...

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