Microblog emotion classifier establishing method and device

Abstract

The invention discloses a microblog emotion classifier establishing method and device. The microblog emotion classifier establishing method comprises the following steps that multiple microblog data is obtain; word segmentation is conducted on microblog texts to obtain a word set; word-word emotion association relation and word-emotion association relation are obtained according to a large number of unannotated microblog texts; artificial annotating is conducted on the preset number of microblog texts to endow emotion labels so as to obtain an annotated microblog emotion data set; the word-word emotion association relation, the word-emotion association relation and the annotated microblog emotion data set are integrated to obtain multi-source heterogeneous emotion information, and a mathematical model is established to obtain a microblog emotion classifier. By the adoption of the method, the microblog emotion classifier can be obtained by fusing the multi-source heterogeneous emotion information, accordingly the emotion tendency expressed by microblog messages are classified, the robustness and accuracy rate of the classifier are improved, the cost is low, and the method is simple and easy to achieve.

Description

technical field [0001] The invention relates to the technical field of network data mining, in particular to a construction method and construction device of a microblog emotion classifier. Background technique [0002] Building sentiment classifiers is an important part of machine learning tasks, among which supervised learning methods are widely used in related researches on building sentiment classifiers. The basic idea is to use the corresponding sentiment data and sentiment labels. , train and optimize the parameters of the classifier model, and finally use the trained model to classify the data without sentiment labels and predict its sentiment category. [0003] In related technologies, a single-dimensional emotional information is generally used to train a classifier, which not only cannot guarantee the robustness and accuracy, but also has high construction cost and low efficiency, which needs to be improved. SUMMARY OF THE INVENTION [0004] The present inventio...

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Problems solved by technology

[0003] In related technologies, the classifier is generally trained with single-dimensional emotional information, which not only cannot guarantee the robustness and accuracy, but also has high construction costs and low efficiency, which needs to be improved.

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