Visual angle level text sentiment classification method and system based on external knowledge

Abstract

The invention relates to a visual angle level text sentiment classification method and system based on external knowledge, and the method comprises the steps: carrying out the characterization of a visual angle level text, and capturing a bidirectional semantic dependence relation of the text; combining the current word with an external knowledge synonym through a dynamic attention mechanism, andintroducing a sentinel vector to avoid misleading of external knowledge to the model; judging the contribution degree of each word to the view word through a position attention mechanism; calculatingan attention score of each memory content, nonlinearly combining the attention score of each layer with an output result of an upper layer by utilizing a threshold circulation unit, and taking the last layer as emotion feature representation of the text; and obtaining a final sentiment classification result by utilizing the classification function. According to the method, the view-level text sentiment classification performance can be improved, and the resource consumption can be reduced.

Description

technical field [0001] The invention relates to the fields of document sentiment analysis, opinion mining and machine learning, in particular to a method and system for classifying text sentiment at the perspective level based on external knowledge. Background technique [0002] Perspective-level text sentiment analysis aims to study the emotional polarity of review texts on a given perspective word, so as to provide a more comprehensive, in-depth and fine-grained analysis than document-level or sentence-level sentiment analysis, which can be widely used in product pricing, Competitive intelligence, stock market prediction and other fields provide people with convenient and automated tools to improve the utilization rate of Internet information. However, user emotion expressions have different performances under different viewing angles. like figure 1 , there are two perspective words "size" and "space" in the text, the emotional polarity shown for the perspective word "si...

AI Technical Summary

Problems solved by technology

The researchers proposed an adaptive recursive neural network (Adaptive Recursive Neural Network, AdaRNN) to model the adaptive propagation of emotional words to specific perspective words. doesn't work

The researchers propose to use the attention mechanism and memory network model to solve the above problems. The memory blocks are constructed through the bidirectional long-short-term memory network and combined with the location information, and then the results of multiple attention are calculated. Finally, the threshold control unit is used for nonlinear combination. Perform perspective-level text sentiment classification. Although this type of method can better handle complex sentences, it lacks consideration of the different meanings of words in different contexts.

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