Association rule-based multi-tag Chinese emotion classification method

Abstract

The invention provides an association rule-based multi-tag Chinese emotion classification method. The method comprises the following steps of: 1, searching a frequent item set among various Chinese emotion tags by utilizing an association rule algorithm; 2, deducing an association rule among the emotion tags according to the frequent item set; 3, modifying a multi-tag data set by using the association rule so as to obtain a new data set; 4, carrying out classified learning on the obtained new data set by using a Rank-SVM algorithm so as to obtain a new model; and 5, predicting a test data set by using the new model.

Description

technical field [0001] The invention relates to a tag classification technology, in particular to a multi-tag Chinese sentiment classification method based on association rules. Background technique [0002] In traditional supervised learning frameworks, each example corresponds to only one class label, and such problems are called single-label learning problems. However, in many real-world settings, an example may not only have a single tag at the same time, but multiple category tags at the same time. For example, in medical diagnosis, a patient may have both diabetes and cancer; in gene function classification, each gene may be associated with a series of functions, such as metabolism, transcription, and protein synthesis; in scene classification, each gene A scene may belong to several semantic categories, such as beach and city. Each of the above examples corresponds to a set of labels, and the size of the label set is uncertain, such problems are called multi-label l...

AI Technical Summary

Problems solved by technology

[0006] Therefore, the existing multi-label learning algorithms rarely apply association rules to multi-label classification, resulting in poor classification results of multi-label learning algorithms.

Images