Text sentiment classification method, system and device and storage medium

Abstract

The invention discloses a text sentiment classification method, system and device and a storage medium. The method comprises the steps of preprocessing a text; performing statistic calculation on thepreprocessed text to obtain a text vector; performing feature selection on the text vectors by adopting a chi-square statistical method, and extracting feature vectors; performing weight calculation on the feature vectors to obtain the weight of each feature vector; and classifying the text based on a support vector machine in combination with the weight of each feature vector. The system comprises a preprocessing module, a statistics module, a feature module, a weight module and a classification module. The device comprises a memory and a processor used for executing the text sentiment classification method. The text classification accuracy can be improved. The text sentiment classification method, system and device and the storage medium can be widely applied to the field of text classification.

Description

technical field [0001] The present invention relates to the field of text classification, in particular to a text sentiment classification method, system, device and storage medium. Background technique [0002] Sentiment classification is a task in the field of natural language processing, also known as tendency analysis, which is the process of analyzing, processing, inducing and reasoning subjective texts with emotional color. It can analyze the author's emotional preferences and opinions on specific subjects in the text, and can be used to predict movie box office, stock trends, public opinion analysis, improve services and products, and understand user experience. The current main research methods for text emotion classification are divided into dictionary-based and corpus-based, information mining on the corpus or dictionary to identify the emotional tendency of words, so as to obtain statistical data and make judgments on their polarity, but these two methods have no ...

AI Technical Summary

Problems solved by technology

It can analyze the author's emotional preferences and opinions on specific subjects in the text, and can be used to predict movie box office, stock trends, public opinion analysis, improve services and products, and understand user experience. The current main research methods for text emotion classification are divided into dictionary-based and corpus-based, information mining on the corpus or dictionary to identify the emotional tendency of words, so as to obtain statistical data and make judgments on their polarity, but these two methods have no part-of-speech discrimination ability for new words, and because they are not from Judgment at the semantic level, the accuracy of classification results is low

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