Cross-domain text sentiment classification method based on domain confrontation self-adaption

Abstract

The invention discloses a cross-domain text sentiment classification method based on domain countermeasure self-adaption. The method comprises the following steps: inputting a word vector matrix, a category label and a domain label of a source domain sample and a target domain sample; Utilizing a feature extraction module based on a convolutional neural network to extract low-level features of thesample; constructing a constraint based on distribution consistency of a source domain and a target domain in a main task module, mapping a low-layer sample to a regeneration kernel Hilbert space, and learning a high-layer feature with transferability; inputting the high-level features of the source domain into a class classifier, and ensuring that the classifier has class discrimination on samples on the basis of reducing domain difference; a domain invariance constraint based on adversarial learning is constructed in an auxiliary task module, and low-level features are input into a domain classifier with adversarial properties, so that the classifier cannot judge the domain to which a sample belongs as much as possible, high-level features with domain invariance are extracted, and the migration problem of a source domain classifier to a target domain is effectively solved.

Description

technical field [0001] The invention belongs to the technical field of text analysis, and in particular relates to a cross-domain text sentiment classification method based on domain confrontation self-adaptation. Background technique [0002] In recent years, with the vigorous development of artificial intelligence and machine learning technology, text sentiment classification technology has emerged as the times require. This technology can automatically classify the sentiment trend of text data, effectively solving the time-consuming and laborious problem of manual judgment. Traditional text sentiment classification methods usually use calibration data to train specific sentiment classifiers for a certain field to complete sentiment classification tasks. However, with the development of social media, the increasing number of new corpus gradually expands the scope of domains, and the amount of data in each domain is very large. Traditional text sentiment classification meth...

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Problems solved by technology

However, with the development of social media, the increasing number of new corpus gradually expands the scope of domains, and the amount of data in each domain is very large. Traditional text sentiment classification methods need to manually label a large amount of data for each newly added domain. Completing the training of the sentiment classifier, the process of manually labeling samples is inefficient

At the same time, with the passage of time and the development of society, the new feature words in the known field will gradually increase. Because there are certain differences in the feature distribution between the original sample and the new sample, the original sentiment classifier in this field will not be able to accurately predict the new comment data. emotional tendency

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