Comment text emotion classification model training and emotion classification method and device and equipment

Abstract

The invention discloses a comment text emotion classification model training and emotion classification method and device and equipment and belongs to the field of text emotion classification in natural language processing. Model training comprises the steps that a comment text and associated subject and object information are acquired; a comment subject and object attention mechanism is fused based on a first-layer Bi-LSTM network to extract sentence-level feature representation; the comment subject and object attention mechanism is fused based on a second-layer Bi-LSTM network to extract document-level feature representation; and a hyperbolic tangent non-linear mapping function is adopted to map document-level features to an emotion category space, softmax classification is adopted to train parameters in a model, and an optimal text emotion classification model is obtained. According to the method, the hierarchical bidirectional Bi-LSTM network model and the attention mechanism are adopted, context semantic robust perception and semantic expression of the text can be realized, the robustness of text emotion classification can be remarkably improved, and the correct rate of classification is increased.

Description

technical field [0001] The present invention relates to comment text sentiment classification, in particular to a comment text sentiment model training and sentiment classification method, device and equipment based on hierarchical bidirectional LSTM (Bidirectional Long Short-Term Memory, Bi-LSTM) and attention mechanism, which belongs to natural language processing technology field. Background technique [0002] The core issue of text sentiment classification is how to effectively represent the emotional semantics of text. With the rapid development of Internet technology, a large number of users have generated valuable comment text information on hot events and products on the Internet, such as Weibo, e-commerce platforms, catering platforms, and so on. These comments contain people's rich emotional colors and emotional tendencies. The purpose of sentiment analysis is to automatically extract and classify users' subjective emotional information on products or events from...

AI Technical Summary

Problems solved by technology

[0004] However, most of the current neural network-based text sentiment classification models only consider the emotional semantics related to the text content, ignoring the subject information of the review related to the text and the review object information described by the text content.

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