Cross-domain sentiment classification method based on comparison and alignment network

Abstract

The invention relates to a cross-domain sentiment analysis method based on a comparison and alignment network, and belongs to the technical field of fine-grained sentiment analysis in natural language processing. According to the method, a scene, which is not fully explored, of cross-domain sentiment classification is researched, that is, a target domain is a scene with few samples. In the scene, the invention provides a neural network model named as a contrast alignment network (CAN). According to the model, two instances are randomly extracted from an original domain and a target domain, and then the target domain and the original domain are trained according to the instances of the combined target domain and the original domain. The first objective is to minimize classification errors on the original domain. The second is a pair of contrast targets, where a measure of distance between a target domain instance and an original domain instance in one pair is minimized if they express the same emotion, otherwise the measure is maximized with a constant upper limit. According to the method, the problem that the target domain data resources in the cross-domain sentiment classification task are limited is solved, and the user experience is improved.

Description

technical field [0001] The invention relates to a cross-domain sentiment classification method, in particular to a cross-domain sentiment analysis method based on a contrast alignment network, and belongs to the technical field of fine-grained sentiment analysis in natural language processing. Background technique [0002] Cross Domain Sentiment Classification (CDSC) is an important task that aims to transfer the learned knowledge from the original domain to the target domain. CDSC enables sentiment classification models trained in the original domain with large amounts of labeled data to perform well on target domain data with limited training samples. When the data in the target domain is lacking and the data in the original domain is sufficient, this situation is more common and challenging in the industry, and the main challenge lies in the domain transfer (or distribution transfer) between the source domain and the target domain. The domain transfer problem is mainly a...

AI Technical Summary

Problems solved by technology

However, in practical applications, the large amount of unlabeled target domain data required by UDA may not be sufficiently available, thus limiting the applicability of UDA

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