Text sentiment analysis method and device, computer device and readable storage medium

Abstract

The invention discloses a text sentiment analysis method and device, a computer device and a computer readable storage medium. The method comprises the following steps: extracting a plurality of target articles from a preset corpus by utilizing a preset extraction rule; carrying out sentiment classification on the statement of each target article by utilizing a pre-established sentiment word segmentation dictionary; according to the sentiment classification result of the sentiment, carrying out sentiment scoring on the sentiment of each target article; obtaining an emotion classification result of each target article based on the emotion scoring condition of the statement in each target article; processing each target article in a preset mode to obtain text data after word segmentation; obtaining training data with emotion classification labels according to the text data of each target article and the emotion classification result of each target article, and obtaining an emotion classification model through training based on the training data; and performing sentiment classification on a to-be-processed article by utilizing the sentiment classification model. Article emotion can beaccurately analyzed and classified.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a text sentiment analysis method, device, computer device and computer-readable storage medium. Background technique [0002] With the rapid development of the mobile Internet, news information is also increasing geometrically. How to quickly understand the overall opinion trend of news in a certain field is a topic worth studying. At the same time, the sentiment analysis of news content can also monitor and control the news more effectively, which is a direction worthy of research. The current methods for text sentiment analysis mainly include unsupervised learning methods based on sentiment lexicons and supervised learning methods based on machine learning algorithms. The unsupervised learning method based on the emotional dictionary does not use training data and has strong generalization to different fields, but the recognition accuracy rate for specif...

AI Technical Summary

Problems solved by technology

The unsupervised learning method based on the emotional dictionary does not use training data and has strong generalization to different fields, but the recognition accuracy rate for specific fields is not satisfactory, while the supervised learning method based on machine learning algorithms requires a large number of labels Training data and feature extraction, and weak generalization to new and unknown corpus

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