Short Text Classification Based on Improved TF-IDF Integrated with Binary Grey Wolf Optimization
To improve the classification accuracy and decrease the feature dimension of special type short text,short text classification based on improved TF-IDF method integrated with Binary Gray Wolf Optimization(BGWO)is proposed.To improve the accuracy of feature vector text weight calculation,likes ranking factor is proposed,and text feature concentration is integrated to calculate the weight of special types of text with a number of likes,and the improved TF-IDF-RANK is designed to weight the features.Meanwhile,based on the initial selection of feature vectors,the BGWO algorithm is designed and optimized to search for the optimal feature subset,and the attenuation coefficient vector and multi-optimal solution iteration mechanism are introduced to improve the performance of gray wolf search.The results show that the proposed method effectively improves the weighting accuracy,better characterizes the primitive feature vectors,enhances the ability to find the global optimal solution during feature selection,and thus improves the classification effect of short text.Tested by LABIC and Tiktok open platform dataset,the Fl value of the comprehensive index is improved by 14.76％and 14.02％respectively,which verifies the effectiveness of the proposed method for the classification of special types of text.
short text classificationfeature weightingTF-IDF-RANK methodfeature selectionbinary gray wolf optimization
万方数据
维普
