Social media data feature selection method fusing L1 regularization and link attributes

Abstract

The invention belongs to the field of data analysis, and particularly relates to a social media data feature selection method fusing L1 regularization and link attributes, which comprises the following steps: S1, inputting social media data, and listing behavior samples of the social media data as features corresponding to social content; s2, representing social media data in a standardized manner; s3, extracting link relationships of four common social media data; s4, realizing feature selection under a corresponding link relation in combination with L1 regularization; s5, feature subsets obtained according to different link relations are extracted and output, and a final feature set is obtained. Social media data feature selection with link attributes is realized, social media data association features which cannot be solved by a traditional feature selection method are solved, and a solid foundation is laid for subsequent operations such as dimension reduction of large-scale social media data and important feature analysis.

Description

technical field [0001] The invention belongs to the field of data analysis, and in particular relates to a feature selection method for social media data that integrates L1 regularization and link attributes. Background technique [0002] The current development of countless social media services enables people to communicate and express themselves conveniently and easily, such as Weibo. The widespread use of social media has generated massive data at an unprecedented speed. For example, hundreds of millions of microblogs are sent and reposted every day. Massive, high-dimensional social media data poses new challenges for data mining tasks such as classification and clustering. Feature selection is widely used in high-dimensional data mining. Traditional feature selection methods, such as L1 regularization, aim to select relevant features from high-dimensional data to obtain concise and accurate data representation, which can reduce dimensionality. Disaster, speed up the le...

AI Technical Summary

Problems solved by technology

The nature of social media data brings new challenges to feature selection, and traditional feature selection methods cannot take advantage of the additional information in linked data

In addition, the nature of social media also determines that its data is massive, noisy and incomplete, which makes the already challenging problem of feature selection for social media link data even more severe.

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