Text sentiment classification method and device, equipment and storage medium

Abstract

The invention provides a text sentiment classification method and a device, equipment and a storage medium. The method comprises the steps of obtaining a word vector in to-be-processed text data, andextracting a feature vector corresponding to the word vector; extracting context feature representation of the feature vector by adopting a bi-directional long-short term memory network Bi-LSTM model;according to the extracted context feature representation, utilizing an Attention mechanism, then introducing a top-k-max pooling processing mode to fully extract text feature representation, and theextracted features are sent to a classifier to obtain higher accuracy. According to the method, the classification accuracy of text sentiment classification is improved, and the classification effectis good.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a text sentiment classification method, device, equipment and storage medium. Background technique [0002] With the development of the Internet and the increase of Internet users, Internet users generate a large amount of text information on the Internet, such as comments on certain commodities, movies, shops, etc. How to dig out useful information from these texts is not only important for merchants and consumers. etc. are beneficial. Therefore, text sentiment analysis becomes particularly important. Text sentiment analysis (i.e., text sentiment classification) is a branch of the field of Natural Language Processing (NLP). Traditional text sentiment classification mainly includes: The text sentiment classification method based on the dictionary and the text sentiment classification method based on machine learning, the above two methods do not consider the context in...

AI Technical Summary

Problems solved by technology

Therefore, text sentiment analysis becomes particularly important. Text sentiment analysis (i.e., text sentiment classification) is a branch of the field of Natural Language Processing (NLP). Traditional text sentiment classification mainly includes: The text sentiment classification method based on the dictionary and the text sentiment classification method based on machine learning, the above two methods do not consider the context information of words or the word order of the text and require a lot of manpower to extract text features, and may not be able to extract deeper text importan...

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