A deep learning multi-category sentiment analysis model combined with attention mechanism

Abstract

The invention relates to a deep learning multi-category sentiment analysis model combined with an attention mechanism, which belongs to the technical field of natural language processing. The invention analyzes the weaknesses of the existing CNN network and LSTM network in text sentiment analysis, and proposes a combination of attention A deep learning multi-category sentiment analysis model based on force mechanism. The model uses the attention mechanism to integrate the local features extracted by the CNN network and the word order features extracted by the LSTM model, and adopts the idea of ​​an integrated model at the classification layer to splicing the emotional features extracted by the CNN network and the LSTM network respectively as the final extraction of the model. emotional characteristics. Through comparative experiments, it is found that the accuracy of the model has been significantly 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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