Deep learning multi-classification emotion analysis model combined with attention mechanism

Abstract

The invention relates to a deep learning multi-classification emotion analysis model combined with an attention mechanism, belongs to the technical field of natural language processing. The invention analyzes weakness of an existing CNN network and an existing LSTM network in the aspect of text emotion analysis, and provides the deep learning multi-classification emotion analysis model combined with the attention mechanism. According to the model, local features extracted by a CNN network and word order features extracted by an LSTM model are fused by using the attention mechanism, and emotion features extracted by the CNN network and the LSTM network are spliced on a classification layer by using an idea of an integrated model to serve as emotion features finally extracted by the model. Through a contrast experiment, the accuracy of the model is obviously improved.

Description

technical field [0001] The invention belongs to the field of text information processing, and relates to a deep learning multi-category sentiment analysis model combined with an attention mechanism. Background technique [0002] With the continuous rise of social networks such as Weibo and Twitter, the Internet is not only a source for people to obtain daily information, but also an indispensable platform for people to express their opinions. People comment on hot events in online communities, express opinions on film reviews, and describe product experience, etc., which will generate a large amount of text information with emotional color (such as: emotions, etc.), and effective sentiment analysis of these text information can be more effective. Get a better understanding of the user's interest tendencies and attention levels. However, with the increase of people's attention to network information, a large number of texts with emotional color are produced in the network co...

AI Technical Summary

Problems solved by technology

However, when the sample data is long or the language scene is complex, the intervals of useful emotional information vary in size and length, and the performance of Long Short-Term Memory (LSTM) is therefore limited.

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