Aiming at the problem of short text feature sparsity

a short text sparse representation classification method based on entropy weighted constraint is proposed. Considering that the initial dictionary dimension is high

firstly

the word in the dictionary is represented as a word vector form via using the Word2vec tool

and then the original dictionary is reduced according to the average weighted vectors. Secondly

a fast feature subset selection algorithm is adopted to remove the irrelevant and redundant short texts in the dictionary

and the filtered dictionary can then be obtained. Thirdly

based on the sparse representation theory

an improved entropy-weighted sparse representation method is designed for the objective function

and the Lagrange multiplier method is introduced to obtain the optimal value of the objective function

thus the subspace of each class is obtained. Finally

the distance between the short text to be classified and the short text in each class is calculated under the subspace

and the short text is classified according to three classification rules. A large number of experimental results on real data sets show that the proposed method can effectively alleviate the short text feature sparse problem and exhibits better performance than the existing short text classification methods.