Research of Text Classification Model Based on Latent Semantic Analysis and Improved HS-SVM

A text classification model based on Latent Semantic Analysis and Improved Hyper-sphere Support Vector Machine, is proposed in order to improve the accuracy and efficiency of text classification. Latent Semantic Analysis is used in this model for feature extraction, eliminating the text representation errors caused by synonyms and polysemes, and reducing the dimension of text vector. At the same time, a new decision-making approach based on concentration, is designed for improving the classification of texts in overlapping regions. Experimental results show that the model will give good classification results when the number of classes is small. And the improved algorithm is effective and feasible.